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Heed, Ingrid, and Karl Lindberg. "Forecasting COVID-19 hospitalizations using dynamic regression with ARIMA errors." Thesis, Uppsala universitet, Statistiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-446310.
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For more than a year, COVID-19 has changed societies all over the world and put massive strains on its healthcare systems. In an attempt to aid in prioritizing medical resources, this thesis uses dynamic regression with ARIMA errors to forecast the number of hospitalizations related to COVID-19 two weeks ahead in Uppsala County. For this purpose, 100 models are created and their ability to forecast hospitalizations two weeks ahead for weeks 15-17 of 2021 for the different municipalities in Uppsala County is evaluated using root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The best performing models are then utilized to forecast hospitalizations for weeks 19-22. The results show that the models perform well during periods of increasing numbers of hospitalizations during early 2021, while they perform less well during the last weeks of May 2021 where hospitalizations numbers have been falling dramatically. This recent decrease in forecasting performance is believed to be caused by an increase in vaccination coverage, which is not accounted for in the models.
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Stocker, Toni Clemens. "On the asymptotic properties of the OLS estimator in regression models with fractionally integrated regressors and errors." [S.l. : s.n.], 2008. http://nbn-resolving.de/urn:nbn:de:bsz:352-opus-57370.
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Oleksandra, Shovkun. "Some methods for reducing the total consumption and production prediction errors of electricity: Adaptive Linear Regression of Original Predictions and Modeling of Prediction Errors." Thesis, Linnéuniversitetet, Institutionen för matematik (MA), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-34398.
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Balance between energy consumption and production of electricityis a very important for the electric power system operation and planning. Itprovides a good principle of effective operation, reduces the generation costin a power system and saves money. Two novel approaches to reduce thetotal errors between forecast and real electricity consumption wereproposed. An Adaptive Linear Regression of Original Predictions (ALROP)was constructed to modify the existing predictions by using simple linearregression with estimation by the Ordinary Least Square (OLS) method.The Weighted Least Square (WLS) method was also used as an alternativeto OLS. The Modeling of Prediction Errors (MPE) was constructed in orderto predict errors for the existing predictions by using the Autoregression(AR) and the Autoregressive-Moving-Average (ARMA) models. For thefirst approach it is observed that the last reported value is of mainimportance. An attempt was made to improve the performance and to getbetter parameter estimates. The separation of concerns and the combinationof concerns were suggested in order to extend the constructed approachesand raise the efficacy of them. Both methods were tested on data for thefourth region of Sweden (“elområde 4”) provided by Bixia. The obtainedresults indicate that all suggested approaches reduce the total percentageerrors of prediction consumption approximately by one half. Resultsindicate that use of the ARMA model slightly better reduces the total errorsthan the other suggested approaches. The most effective way to reduce thetotal consumption prediction errors seems to be obtained by reducing thetotal errors for each subregion.
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Nováčková, Monika. "Aplikace analýzy časových řad v prognózování." Master's thesis, Vysoká škola ekonomická v Praze, 2013. http://www.nusl.cz/ntk/nusl-199538.
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This thesis attempts to predict daily number of firefighter incidents in the Central Bohemia Region and in the Region of Hradec Králové to improve firefighter shift planning. The analysis is based on a dataset of firefighter incidents from the period between the years 2008 and 2012. Econometric models, capturing yearly and weekly patterns and weather impact were estimated and used for long-term prediction. The first part of the thesis provides a description of tests applied to residuals and other econometric tests used in this study. Then linear regression is applied to model weather impact and effects of days of week and months of year. In the next part regression with AR errors, (S)ARMA models and regression with (S)ARMA errors are estimated. All these models are compared according to properties of residuals and out-of-sample mean absolute percentage error (MAPE). The most accurate models predict daily number of incidents two months ahead with MAPE slightly above 20% which is considerably better than the benchmark Holt-Winters method. Regression models with (S)ARMA errors produce relatively accurate long-term forecasts and its error terms are uncorrelated. Therefore, they can be considered suitable for long-term prediction of firefighter incidents.
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Liendeborg, Zaida, and Mattias Karlsson. "Prognostisering av försäljningsvolym med hjälp av omvärldsindikatorer." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-129572.
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Background Forecasts are used as a basis for decision making and they mainly affect decisions at strategic and tactical levels in a company or organization. There are two different methods to perform forecasts. The first one is a qualitative method where a n expert or group of experts tell about the future. The second one is a quantitative method where forecast are produced by mathematical and statistical models. This study used a quantitative method to build a forecast model and took into account external f actors in forecasting the sales volume of Bosch Rexroth’s hydraulic motors. There is a very wide range of external factors and only a limited selection had been analyzed in this study. The selection of the variables was based on the markets where Bosch Rexroth products are used, such as mining. Purpose This study aimed to develop five predictive models: one model for the global sales volume, one model each for sales volume in USA and China and one model each for sales volume of CA engine and Viking engine. By identifying external factors that showed significant relationship in various time lags with Bosch Rexroth’s sales volume, the forecasts 2016 and 2017 were produced. Methods The study used a combination of multiple linear regression and a Box - Jenkins AR MA errors to analyze the association of external factors and to produce forecasts. Externa l factors such as commodity prices, inflation and exchange rates between different currencies were taken into account. By using a cross - correlation function between external factors and the sales volume, significant external factors in different time lags were identified and then put into the model. The forecasting method used is a Causal forecasting model. Conclusions The global sales volume of Bosch Rexroth turned out to be affected by the historical price of copper in three different time lags , one, six and seven months . From 2010 to 2015, the copper price have been continuously dropping which explain s the downward trend of the sales volume. The sales volume in The U SA showed a significant association by the price of coal with three and four time lags. This means that the change of coal price takes three and four months before it affects the sales volume in the USA. The market in China showed to be affected by the development of the price of silver. The volume of sales is affected by the price of silver by four and six time lags. CA engine also displayed association with the price of copper at the same time lags as in the global sales volume. On the other hand, Viking engine showed no significant association at all with any of the external factors that were analyzed in this study. The forecast for global mean sales volume will be between 253 to 309 units a month for May 2016 – December 2017. Mean sales volume in USA projected to be in between 24 to 32 units per month. China's mean sales volume is expected to be in between 42 to 81 units a month. Mean sales volume of CA engine has a forecast of 175 to 212 units a month. While the mean s ales of Viking engine projected to stay in a constant volume of 25 units per month.
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Nayeri, Negin. "Option strategies using hybrid Support Vector Regression - ARIMA." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-275719.
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In this thesis, the use of machine learning in option strategies is evaluated with focus on the S&P 500 Index. The first part of the thesis focuses on testing the performance power of the Support Vector Regression (SVR) method for the historical realized volatility with a window of 20 days. The prediction window will also be 1-month forward (approximately 20 trading days). The second part of the thesis focuses on creating an ARIMA model that forecasts the error that is based on the difference between the predicted respective true values. This is done in order to create the hybrid SVR-ARIMA model. The new model now consists of a realized volatility value derived from the SVR model as well as the error obtained from the ARIMA model. Lastly, the two methods, that is single SVR and hybrid SVR-ARIMA are compared and the model that exhibits the best result is used within two option strategies. The results showcase the promising forecasting power of the SVR method which for this dataset had an accuracy leveland 67 % for the realized volatility. The ARIMA model also exhibits successful forecasting ability for the next lag. However, for this dataset, the Hybrid SVR-ARIMA model outperforms the single SVR model. It is debatable whether the success of these methods may be due to the fact the dataset only covers the years between 2010-2018 and the highly volatile environments of the financial crisis 2008 is omitted. Nonetheless, the use of the hybrid SVR-ARIMA model used within the two option strategies gives an average payoff 0.37 % and 1.68 %. It should however be noted that the affiliated costs of trading options is not included in the payoff and neither is the cost of premium due in buying options as the costs vary depending on the origin of the purchase. This thesis has been performed in collaboration with Crescit Asset Management in Stockholm, Sweden.<br>I denna uppsats utvärderas användningen av maskininlärning i optionsstrategier med fokus på S&P 500 Index. Den första delen av uppsatsen fokuserar på att testa prognos kraften av Support Vector Regression (SVR) metoden för den realiserade volatiliteten med ett fönster på 20 dagar. Prognos kommer att ske för 1 månad framåt (20 trading dagar). Den andra delen av uppsatsen fokuserar på att skapa en ARIMA-modell som prognostiserar nästa värdet i tidsserien som baseras på skillnaden mellan de erhållna prognoserna samt sanna värdena. Detta görs för att skapa en hybrid SVR-ARIMA-modell. Den nya modellen består nu av ett realiserat volatilitetsvärde härrörande från SVR samt den error som erhållits från ARIMA- modellen. Avslutningsvis kommer de två metoderna, det vill säga SVR och hybrid SVR-ARIMA, jämföras och den modell med bäst resultat användas inom två options strategier. Resultaten visar den lovande prognotiseringsförmågan för SVR-metoden som för denna dataset hade en noggrannhetsnivå på 67 % för realiserad volatiliteten. ARIMA- modellen visar också en framgångsrik prognosförmåga för nästa punkt i tidsserien. Dock överträffar Hybrid SVR-ARIMA-modellen SVR-modellen för detta dataset. Det kan diskuteras ifall framgången med dessa metoder kan bero på att denna dataset täcker åren mellan 2010-2018 och det mycket volatila tiden under finanskrisen 2008 är uteslutet. Detta kan ifrågasätta modellernas prognotiseringsförmåga på högre volatilitetsmarknader. Dock ger användningen av hybrid-SVR-ARIMA-modellen som används inom de två option strategierna en genomsnittlig avkastning på 0,37 % och 1,68 %. Det bör dock noteras att de tillkommande kostnaderna för att handla optioner samt premiekostnaden som skall betalas i samband med köp av optioner inte ingår i avkastningen då dessa kostnader varierar beroende på plats av köp. Denna uppsats har gjorts i samarbete med Crescit Asset Management i Stockholm.
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Nilsson, Fredrik. "Payment Volume Forecasting using Hierarchical Regression with SARIMA Errors : Payment Volume Forecasting using Hierarchical Regression with SARIMA Errors." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-425886.
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When forecasting financial transaction volumes in different markets, different markets often exhibit similar seasonality patterns and public holiday behavior. In this thesis, an attempt is made at utilizing these similarities to improve forecasting accuracy as compared to forecasting each market individually. Bayesian hierarchical regression models with time series errors are used on daily transaction data. When fitting three years of historic data for all markets, no consistent significant improvements in forecasting accuracy was found over a non-hierarchical regression model. When the amount of historic data was limited to less than one year for a single market, with the other markets having three years of historic data, the hierarchical model significantly outperformed both non-hierarchical and naive reference models on the market with limited historic data.
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Wågberg, Max. "Att förutspå Sveriges bistånd : En jämförelse mellan Support Vector Regression och ARIMA." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-36479.
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In recent years, the use of machine learning has increased significantly. Its uses range from making the everyday life easier with voice-guided smart devices to image recognition, or predicting the stock market. Predicting economic values has long been possible by using methods other than machine learning, such as statistical algorithms. These algorithms and machine learning models use time series, which is a set of data points observed constantly over a given time interval, in order to predict data points beyond the original time series. But which of these methods gives the best results? The overall purpose of this project is to predict Sweden’s aid curve using the machine learning model Support Vector Regression and the classic statistical algorithm autoregressive integrated moving average which is abbreviated ARIMA. The time series used in the prediction are annual summaries of Sweden’s total aid to the world from openaid.se since 1998 and up to 2019. SVR and ARIMA are implemented in python with the help of the Scikit- and Statsmodels libraries. The results from SVR and ARIMA are measured in comparison with the original value and their predicted values, while the accuracy is measured in Root Square Mean Error and presented in the results chapter. The result shows that SVR with the RBF-kernel is the algorithm that provides the best results for the data series. All predictions beyond the times series are then visually presented on a openaid prototype page using D3.js<br>Under det senaste åren har användningen av maskininlärning ökat markant. Dess användningsområden varierar mellan allt från att göra vardagen lättare med röststyrda smarta enheter till bildigenkänning eller att förutspå börsvärden. Att förutspå ekonomiska värden har länge varit möjligt med hjälp av andra metoder än maskininlärning, såsom exempel statistiska algoritmer. Dessa algoritmer och maskininlärningsmodeller använder tidsserier, vilket är en samling datapunkter observerade konstant över en given tidsintervall, för att kunna förutspå datapunkter bortom den originella tidsserien. Men vilken av dessa metoder ger bäst resultat? Projektets övergripande syfte är att förutse sveriges biståndskurva med hjälp av maskininlärningsmodellen Support Vector Regression och den klassiska statistiska algoritmen autoregressive integrated moving average som förkortas ARIMA. Tidsserien som används vid förutsägelsen är årliga summeringar av biståndet från openaid.se sedan år 1998 och fram till 2019. SVR och ARIMA implementeras i python med hjälp av Scikit-learn och Statsmodelsbiblioteken. Resultatet från SVR och ARIMA mäts i jämförelse mellan det originala värdet och deras förutspådda värden medan noggrannheten mäts i root square mean error och presenteras under resultatkapitlet. Resultatet visar att SVR med RBF kärnan är den algoritm som ger det bästa testresultatet för dataserien. Alla förutsägelser bortom tidsserien presenteras därefter visuellt på en openaid prototypsida med hjälp av D3.js.
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Gillard, Jonathan William. "Errors in variables regression : what is the appropriate model?" Thesis, Cardiff University, 2007. http://orca.cf.ac.uk/54629/.
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The fitting of a straight line to bivariate data (x,y) is a common procedure. Standard linear regression theory deals with the situation when there is only error in one variable, either x, or y. A procedure known as y on x regression fits a line where the error is assumed to be associated with the y variable, alternatively, x on y regression fits a line when the error is associated with the x variable. The model to describe the scenario when there are errors in both variables is known as an errors in variables model. Errors in variables modelling is fundamentally different from standard regression techniques. The problems of model fitting and parameter estimation of a straight line errors in variables model cannot be solved by generalising a simple linear regression model. Briefly, this thesis provides a unified framework to the fitting of a straight line errors in variables model using the method of moments. Estimators of the line using a higher moments approach have been detailed, and asymptotic variance covariance matrices of a plethora of slope estimators are provided. Simulations demonstrate that these variance covariance matrices are accurate for even small data sets. The topic of prediction is considered, with an estimator for the latent variable presented, as well as advice on the mean value of y given x via both a parametric and non-parametric approach. The problem of residuals in an errors in variables model is described, and some quick solutions given. Some examples are presented towards the end of this thesis to demonstrate how the ideas provided may be applied to real-life data sets, as well as some areas which may demand further research.
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Gunby, James Alexander. "Measurement errors in case-control and related studies." Thesis, University of Oxford, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.239324.
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Chen, Kun. "Regularized multivariate stochastic regression." Diss., University of Iowa, 2011. https://ir.uiowa.edu/etd/1209.
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In many high dimensional problems, the dependence structure among the variables can be quite complex. An appropriate use of the regularization techniques coupled with other classical statistical methods can often improve estimation and prediction accuracy and facilitate model interpretation, by seeking a parsimonious model representation that involves only the subset of revelent variables. We propose two regularized stochastic regression approaches, for efficiently estimating certain sparse dependence structure in the data. We first consider a multivariate regression setting, in which the large number of responses and predictors may be associated through only a few channels/pathways and each of these associations may only involve a few responses and predictors. We propose a regularized reduced-rank regression approach, in which the model estimation and rank determination are conducted simultaneously and the resulting regularized estimator of the coefficient matrix admits a sparse singular value decomposition (SVD). Secondly, we consider model selection of subset autoregressive moving-average (ARMA) modelling, for which automatic selection methods do not directly apply because the innovation process is latent. We propose to identify the optimal subset ARMA model by fitting a penalized regression, e.g. adaptive Lasso, of the time series on its lags and the lags of the residuals from a long autoregression fitted to the time-series data, where the residuals serve as proxies for the innovations. Computation algorithms and regularization parameter selection methods for both proposed approaches are developed, and their properties are explored both theoretically and by simulation. Under mild regularity conditions, the proposed methods are shown to be selection consistent, asymptotically normal and enjoy the oracle properties. We apply the proposed approaches to several applications across disciplines including cancer genetics, ecology and macroeconomics.
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Tabatabaey, S. M. Mehdi (Seyed Mohammad Mehdi) Carleton University Dissertation Mathematics and Statistics. "Preliminary test approach estimation: regression model with spherically symmetric errors." Ottawa, 1995.
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Chan, Shih-huang. "Polynomial spline regression with unknown knots and AR(1) errors." The Ohio State University, 1989. http://rave.ohiolink.edu/etdc/view?acc_num=osu1340986578.
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Xie, Chuanlong. "Model checking for regressions when variables are measured with errors." HKBU Institutional Repository, 2017. https://repository.hkbu.edu.hk/etd_oa/445.
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In this thesis, we investigate model checking problems for parametric single-index regression models when the variables are measured with different types of errors. The large sample behaviours of the test statistics can be used to develop properly centered and scaled model checking procedures. In addition, a dimension reduction model-adaptive strategy is employed, with the special requirements for the models with measurement errors, to improve the proposed testing procedures. This makes the test statistics converge to their weak limit under the null hypothesis with the convergence rates not depending on the dimension of predictor vector. Furthermore, the proposed tests behave like a classical local smoothing test with only one-dimensional predictor. Therefore the proposed methods have potential for alleviating the difficulties associated with high dimensionality in hypothesis testing.. Chapter 2 provides some tests for a parametric single-index regression model when predictors are measured with errors in an additive manner and validation dataset is available. The two proposed tests have consistency rates not depending on the dimension of predictor vector. One of these tests has a bias term that may become arbitrarily large with increasing sample size, but has smaller asymptotic variance. The other test is asymptotically unbiased with larger asymptotic variance. Both are still omnibus against general alternatives. Besides, a systematic study is conducted to give an insight on the effect of the ratio between the size of primary data and the size of validation data on the asymptotic behavior of these tests. Simulation studies are carried out to examine the finite-sample performances of the proposed tests. Also the tests are applied to a real data set about breast cancer with validation data obtained from a nutrition study.. Chapter 3 introduces a minimum projected-distance test for a parametric single-index regression model when predictors are measured with Berkson type errors. The distribution of the measurement error is assumed to be known up to several parameters. This test is constructed by combining the minimum distance test with a dimension reduction model-adaptive strategy. After properly centering, the minimum projected-distance test statistic is asymptotically normal at a convergence rate of order nh^(1/2) and can detect a sequence of local alternatives distinct from the null model with a rate of order n^(-1/2) h^(-1/4) where n is the sample size and h is a sequence of bandwidths tending to 0 as n tends infinity. These rates do not depend on the dimensionality of predictor vector, which implies that the proposed test has potential for alleviating the curse of dimensionality in hypothesis testing in this field. Further, as the test is asymptotically biased, two bias-correction methods are suggested to construct asymptotically unbiased tests. In addition, we discuss some details in the implementation of the proposed tests and then provide a simplified procedure. Simulations indicate desirable finite-sample performances of the tests. Besides, we illustrate the proposed model checking procedures by using two real datasets to illustrate the effects of air pollution on Emphysema.. Chapter 4 provides a nonparametric test for checking a parametric single-index regression model when predictor vector and response are measured with distortion errors. We estimate the true values of response and predictor, and then plug the estimated values into a test statistic to develop a model checking procedure. The dimension reduction model-adaptive strategy is also employed to improve its theoretical properties and finite sample performance. Another interesting observation in this work is that, with properly selected bandwidths and kernel functions in a limited range, the proposed test statistic has the same limiting distribution as that under the classical regression setup without distortion measurement errors. Simulation studies are conducted.
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Zimmer, Zachary. "Predicting NFL Games Using a Seasonal Dynamic Logistic Regression Model." VCU Scholars Compass, 2006. http://scholarscompass.vcu.edu/etd_retro/97.
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The article offers a dynamic approach for predicting the outcomes of NFL games using the NFL games from 2002-2005. A logistic regression model is used to predict the probability that one team defeats another. The parameters of this model are the strengths of the teams and a home field advantage factor. Since it assumed that a team's strength is time dependent, the strength parameters were assigned a seasonal time series process. The best model was selected using all the data from 2002 through the first seven weeks of 2005. The last weeks of 2005 were used for prediction estimates.
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zhang, fan. "Test of Equality Between Regression Lines in Presence of Errors in Variables." Thesis, Uppsala universitet, Statistiska institutionen, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-175809.
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Chen, Shu. "Some limit behaviors for the LS estimators in errors-in-variables regression model." Kansas State University, 2011. http://hdl.handle.net/2097/12206.
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Master of Science<br>Department of Statistics<br>Weixing Song<br>There has been a continuing interest among statisticians in the problem of regression models wherein the independent variables are measured with error and there is considerable literature on the subject. In the following report, we discuss the errors-in-variables regression model: yi = β0 + β1xi + β2zi + ϵi,Xi = xi + ui,Zi = zi + vi with i.i.d. errors (ϵi, ui, vi), for
i = 1, 2, ..., n and find the least square estimators for the parameters of interest. Both weak and strong consistency for the least square estimators βˆ0, βˆ1, and βˆ2 of the unknown parameters β0, β1, and β2 are obtained. Moreover, under regularity conditions, the asymptotic normalities of the estimators are reported.
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Klasson, Svensson Emil, and Anton Persson. "En statistisk analys av islastens effekt på en dammkonstruktion." Thesis, Linköpings universitet, Statistik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-129963.
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En damm används i huvudsak för att magasinera vatten i energiutvinningssyfte. Dammen rör sig fram och tillbaka i ett säsongsmönster mestadels beroende på skillnader i utomhustemperatur och vattentemperaturen i magasinet. Det nordiska klimatet innebär risk för isläggning i magasinet, för vilken lasten är relativt outforskad. Denna rapport syftar till ett med multipla linjära regressionsmodeller samt dynamiska regressionsmodeller avgöra vilka variabler som förklarar en specifik svensk dammkonstruktions rörelse. Dammens rörelse mäts genom att mäta dammens förflyttning kontra berggrunden med data från dammens inverterade pendlar. Av särskilt intresse är att avgöra islastens påverkan på rörelsen. Resultaten visar att multipla linjära regressions-modeller inte fullständigt lyckas modellera dammens rörelse, då de har problem med autokorrelerade residualer. Detta hanteras med hjälp av autoregressiva regressionsmodeller där de initiala förklarande variablerna inkluderas, kallat dynamisk regression. Denna rapports resultat visar att de autoregressiva parametrarna fungerar mycket väl för att förklara pendlarna, men att även tid, temperatur, det hydrostatiska trycket samt istjocklek är användbara förklarande variabler. Istjockleken visar signifikant påverkan på 5 % signifikansnivå på två av de undersökta pendlarna, vilket är ett noterbart resultat. Författarna menar att rapportens resultat indikerar att det finns anledning att fortsätta forska kring islastens påverkan på dammkonstruktioner.<br>A dam is a structure mainly used for storing water and generating electricity. The structure of a dam moves in a season-based pattern, mainly because of the difference in temperature between the air on outside of the dam and the water on the inside. Due to the Nordic climate, occurrences of icing on the water in the basin is fairly frequent. The effects of ice on the structural load of the dam are relatively unexplored and are the subject to this bachelor’s thesis. The goal of this project is to evaluate which predictors are significant to the movement of the dam with multiple linear regression models and dynamic regressions. The movement is measured by inverted pendulums that register the dam’s movement compared to the foundation. It is of particular interest to determine if the ice load influences the movement of the dam. The multiple regression models used to explain the dam’s movement were all discarded due to autocorrelation in the residuals. This falsifies the models, since autocorrelation means that they don’t meet the needed assumptions. To counteract the autocorrelation, dynamic models with autoregressive terms were fitted. These models showed no problem with autocorrelation. The result from the dynamic models were successful and managed to significantly explain the movement of the dam. The autoregressive terms proved to be efficient explanatory variables. The dynamic regression models also show that the time, temperature, hydrostatic pressure and ice thickness variables are also useful explanatory variables. The ice thickness shows a significant effect at the 5 % significance level on two of the investigated pendulums. The report's results indicate that there is reason to continue research on the ice load impact on dam constructions.
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Bondesson, Matilda, and Josefin Svensson. "Följdinvandring och medborgarskap : en statistisk analys." Thesis, Linköping University, Department of Computer and Information Science, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-19441.
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<p></p><p>During the last years around 100 000 immigrants have arrived to Sweden, people with different reasons and different goals for settling down in Sweden. The reason for immigrating to Sweden that will be dealt with in this thesis is following immigration, i.e. when someone moves here because they have relatives living in the country.</p><p>The reason why it is interesting to study following immigration is that it is an affecting factor for how many that will immigrate to Sweden the following years and may then be used to make a forecast, based on how many first time immigrants there are. To be able to investigate the following immigration analyses are made with time series, logistic regression and Poisson regression. An ARIMA-model has been used to estimate the number of following immigrants in the future.</p><p>The other part of this thesis will inquire the matter how inclined immigrants are to become Swedish citizens, whether they even apply for citizenship and also how long time it takes from the time when they fulfil the conditions for Swedish citizenship until they apply. Here also multiple logistic regression will be used and then ordinary regression.</p><p>The most common reason for permitted residence in Sweden is following immigration. Following immigration has increased since 1998, mainly over the last years there has been a substantial immigration increase. It is difficult to predict how the immigration will develop during the following years due to the occurred growth of immigrants at the end of the study period. Since 1998 about 5% of the persons that have got permitted residence in Sweden are association persons. Most common to be an association person is an older man and the reason he got permitted residence was asylum. The association persons have in average 3,16 following immigrants tied to them.</p><p>To be Swedish citizen through naturalization there are conditions that need to be fulfilled, for example, the immigrant has to have been settled in Sweden for a certain time. For the immigrants that fulfil this time condition there are about 79 % that apply for Swedish citizenship. The largest probability that an immigrant apply for citizenship occur if the person is young, woman and following immigrant. The ones that apply for citizenship are waiting in average 57 days until they are applying after they fulfil the time condition.</p><p> </p><br><p> </p><p>Under 2008 invandrade drygt 100 000 personer till Sverige, personer som invandrade av olika skäl och med olika mål med sin bosättning i Sverige. Den anledning för invandring till Sverige som framförallt behandlas i den här uppsatsen är att man har anhöriga i landet, vilket kallas följdinvandring.</p><p>Anledningen till att det är intressant att studera följdinvandring är att det är en påverkande faktor för hur många som kommer att invandra till Sverige under kommande år och kan alltså användas för prognoser, utifrån hur stort antalet förstagångsinvandrare är. För att kunna undersöka följdinvandringen analyseras den med tidsserier, logistisk regression och Poissonregression. Till skattningar av antalet följdinvandrare i framtiden har en ARIMA-modell anpassats.</p><p>Den andra delen av uppsatsen kommer att undersöka hur benägna invandrare är att bli svenska medborgare. Av intresse är om de alls ansöker om medborgarskap och givet att de gör det hur lång tid det tar ifrån det att de uppfyller villkoren för svenskt medborgarskap till dess att de ansöker. Även här kommer logistisk regression att användas och sedan linjär regression.</p><p>En av de vanligaste anledningarna till att få uppehållstillstånd är att man är följdinvandrare. Följdinvandringen har ökat sedan 1998, framför allt under de senaste åren då en kraftig ökning kan skönjas. Att en så stark ökning inträffar i slutet av perioden gör det svårt att förutsäga hur följdinvandringen kommer utvecklas inom de närmaste åren. Av de personer som sedan 1998 fått uppehållstillstånd i Sverige är idag ungefär 5 % anknytningspersoner. Att bli anknytningsperson är vanligast om man är äldre, man och har fått uppehållstillstånd på grund av asyl. Anknytningspersonerna hade i genomsnitt 3,16 följdinvandrare knutna till sig.</p><p>För att kunna bli svensk medborgare genom naturalisation krävs bland annat att man haft sin hemvist i Sverige under en viss tid. Av dem som uppfyllt tidskravet ansöker ungefär 79 % om medborgarskap. Störst sannolikhet att en person ska ansöka om medborgarskap är det om personen är ung, kvinna och följdinvandrare. De som ansöker om medborgarskap väntar i genomsnitt 57 dagar tills de ansöker efter det att de uppfyllt tidskravet.</p><p> </p><p> </p>
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Fedorov, Valery V., and Werner Müller. "Optimum design for correlated processes via eigenfunction expansions." Institut für Statistik und Mathematik, WU Vienna University of Economics and Business, 2004. http://epub.wu.ac.at/622/1/document.pdf.
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In this paper we consider optimum design of experiments for correlated observations. We approximate the error component of the process by an eigenvector expansion of the corresponding covariance function. Furthermore we study the limit behavior of an additional white noise as a regularization tool. The approach is illustrated by some typical examples. (authors' abstract)<br>Series: Research Report Series / Department of Statistics and Mathematics
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Liu, Yantong. "Robust mixture linear EIV regression models by t-distribution." Kansas State University, 2012. http://hdl.handle.net/2097/15157.
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Master of Science<br>Department of Statistics<br>Weixing Song<br>A robust estimation procedure for mixture errors-in-variables linear regression models is proposed in the report by assuming the error terms follow a t-distribution. The estimation procedure is implemented by an EM algorithm based on the fact that the t-distribution is a scale mixture of normal distribution and a Gamma distribution. Finite sample performance of the proposed algorithm is evaluated by some extensive simulation studies. Comparison is also made with the MLE procedure under normality assumption.
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Tlachac, Monica. "Tackling the Antibiotic Resistant Bacteria Crisis Using Longitudinal Antibiograms." Digital WPI, 2018. https://digitalcommons.wpi.edu/etd-theses/1318.
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Antibiotic resistant bacteria, a growing health crisis, arise due to antibiotic overuse and misuse. Resistant infections endanger the lives of patients and are financially burdensome. Aggregate antimicrobial susceptibility reports, called antibiograms, are critical for tracking antibiotic susceptibility and evaluating the likelihood of the effectiveness of different antibiotics to treat an infection prior to the availability of patient specific susceptibility data. This research leverages the Massachusetts Statewide Antibiogram database, a rich dataset composed of antibiograms for $754$ antibiotic-bacteria pairs collected by the Massachusetts Department of Public Health from $2002$ to $2016$. However, these antibiograms are at least a year old, meaning antibiotics are prescribed based on outdated data which unnecessarily furthers resistance. Our objective is to employ data science techniques on these antibiograms to assist in developing more responsible antibiotic prescription practices. First, we use model selectors with regression-based techniques to forecast the current antimicrobial resistance. Next, we develop an assistant to immediately identify clinically and statistically significant changes in antimicrobial resistance between years once the most recent year of antibiograms are collected. Lastly, we use k-means clustering on resistance trends to detect antibiotic-bacteria pairs with resistance trends for which forecasting will not be effective. These three strategies can be implemented to guide more responsible antibiotic prescription practices and thus reduce unnecessary increases in antibiotic resistance.
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ROSA, Rogério dos Santos. "Associating genotype sequence properties to haplotype inference errors." Universidade Federal de Pernambuco, 2015. https://repositorio.ufpe.br/handle/123456789/16011.
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Previous issue date: 2015-03-12<br>Haplotype information has a central role in the understanding and diagnosis of certain
illnesses, and also for evolution studies. Since that type of information is hard to obtain directly,
computational methods to infer haplotype from genotype data have received great attention
from the computational biology community. Unfortunately, haplotype inference is a very hard
computational biology problem and the existing methods can only partially identify correct
solutions. I present neural network models that use different properties of the data to predict
when a method is more prone to make errors. I construct models for three different Haplotype
Inference approaches and I show that our models are accurate and statistically relevant. The
results of our experiments offer valuable insights on the performance of those methods, opening
opportunity for a combination of strategies or improvement of individual approaches. I formally
demonstrate that Linkage Disequilibrium (LD) and heterozygosity are very strong indicators
of Switch Error tendency for four methods studied, and I delineate scenarios based on LD
measures, that reveal a higher or smaller propension of the HI methods to present inference
errors, so the correlation between LD and the occurrence of errors varies among regions along
the genotypes. I present evidence that considering windows of length 10, immediately to the
left of a SNP (upstream region), and eliminating the non-informative SNPs through Fisher’s
Test leads to a more suitable correlation between LD and Inference Errors. I apply Multiple
Linear Regression to explore the relevance of several biologically meaningful properties of the
genotype sequences for the accuracy of the haplotype inference results, developing models for
two databases (considering only Humans) and using two error metrics. The accuracy of our
results and the stability of our proposed models are supported by statistical evidence.<br>Haplótipos têm um papel central na compreensão e diagnóstico de determinadas doenças
e também para estudos de evolução. Este tipo de informação é difícil de obter diretamente,
diante disto, métodos computacionais para inferir haplótipos a partir de dados genotípicos têm
recebido grande atenção da comunidade de biologia computacional. Infelizmente, a Inferência
de Halótipos é um problema difícil e os métodos existentes só podem predizer parcialmente
soluções corretas. Foram desenvolvidos modelos de redes neurais que utilizam diferentes
propriedades dos dados para prever quando um método é mais propenso a cometer erros. Foram
calibrados modelos para três abordagens de Inferência de Haplótipos diferentes e os resultados
validados estatisticamente. Os resultados dos experimentos oferecem informações valiosas sobre
o desempenho e comportamento desses métodos, gerando condições para o desenvolvimento
de estratégias de combinação de diferentes soluções ou melhoria das abordagens individuais.
Foi demonstrado que Desequilíbrio de Ligação (LD) e heterozigosidade são fortes indicadores
de tendência de erro, desta forma foram delineados cenários com base em medidas de LD, que
revelam quando um método tem maior ou menor propensão de cometer erros. Foi identificado
que utilizando janelas de 10 SNPs (polimorfismo de um único nucleotídeo), imediatamente a
montante, e eliminando os SNPs não informativos pelo Teste de Fisher leva-se a uma correlação
mais adequada entre LD e a ocorrência de erros. Por fim, foi aplicada análise de Regressão Linear
para explorar a relevância de várias propriedades biologicamente significativas das sequências de
genótipos para a precisão dos resultados de Inferência de Haplótipos, estimou-se modelos para
duas bases de dados (considerando apenas humanos) utilizando duas métricas de erro. A precisão
dos resultados e a estabilidade dos modelos propostos foram validadas por testes estatísticos.
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Zhang, Ying, and Hailun Wu. "A comparison of the prediction performances by the linear models and the ARIMA model : Take AUD/JPY as an example." Thesis, Umeå University, Umeå School of Business, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-1047.
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<p>With the development of the financial markets, the foreign exchange market has become more and more important for investors. The daily volume of business dealt with on the foreign exchange markets in 1998 was estimated to be over $2.5 trillion dollars (the daily volume on New York Stock Exchanges is about $20 billion). Today (2006) it may be about $5 trillion dollars. More and more people notice the foreign exchange market, and more and more sophisticated investors research such markets. The purpose of this thesis is to compare different methods to forecast the exchange rate of the money pair AUD/JPY. Firstly we studied the relationship between the AUD/JPY exchange rate and some economic fundamentals by using a regression model. Secondly, we tested whether the AUD/JPY exchange rate had any relationship with its historical records by using an ARIMA model. Finally, we compared the two model forecasting performance. A secondary purpose is to test whether the Market Efficiency Hypothesis works on the money pair AUD/JPY. In the study, data from January 1986 to June 2006 were chosen. To test which method produces better forecasts, we chose data from January 1986 to December 2002 to build up the prediction functions. Then we used the data from January 2003 to 2006 June to evaluate which predicting method was closer to the reality. In the comparison of the forecasting performances, two approaches dealing with the unknown future fundamentals were used. Firstly we assumed that we could do perfect predictions of these regressors, that was, our predictions of these regressors were the same as the actual future outcomes. So we put the real data for the fundamentals from January 2003 to June 2006 into the regression function. Secondly we assumed that we were in real life situation, and we had to predict the regressors first in order to get the predictions of the exchange rate. The results of the comparison were that the AUD/JPY exchange rate could to some extent be predictable, and that the predictions by the ARIMA model were more accurate.</p>
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Althubaiti, Alaa Mohammed A. "Dependent Berkson errors in linear and nonlinear models." Thesis, University of Manchester, 2011. https://www.research.manchester.ac.uk/portal/en/theses/dependent-berkson-errors-in-linear-and-nonlinear-models(d56c5e58-bf97-4b47-b8ce-588f970dc45f).html.
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Often predictor variables in regression models are measured with errors. This is known as an errors-in-variables (EIV) problem. The statistical analysis of the data ignoring the EIV is called naive analysis. As a result, the variance of the errors is underestimated. This affects any statistical inference that may subsequently be made about the model parameter estimates or the response prediction. In some cases (e.g. quadratic polynomial models) the parameter estimates and the model prediction is biased. The errors can occur in different ways. These errors are mainly classified into classical (i.e. occur in observational studies) or Berkson type (i.e. occur in designed experiments). This thesis addresses the problem of the Berkson EIV and their effect on the statistical analysis of data fitted using linear and nonlinear models. In particular, the case when the errors are dependent and have heterogeneous variance is studied. Both analytical and empirical tools have been used to develop new approaches for dealing with this type of errors. Two different scenarios are considered: mixture experiments where the model to be estimated is linear in the parameters and the EIV are correlated; and bioassay dose-response studies where the model to be estimated is nonlinear. EIV following Gaussian distribution, as well as the much less investigated non-Gaussian distribution are examined. When the errors occur in mixture experiments both analytical and empirical results showed that the naive analysis produces biased and inefficient estimators for the model parameters. The magnitude of the bias depends on the variances of the EIV for the mixture components, the model and its parameters. First and second Scheffé polynomials are used to fit the response. To adjust for the EIV, four different approaches of corrections are proposed. The statistical properties of the estimators are investigated, and compared with the naive analysis estimators. Analytical and empirical weighted regression calibration methods are found to give the most accurate and efficient results. The approaches require the error variance to be known prior to the analysis. The robustness of the adjusted approaches for misspecified variance was also examined. Different error scenarios of EIV in the settings of concentrations in bioassay dose-response studies are studied (i.e. dependent and independent errors). The scenarios are motivated by real-life examples. Comparisons between the effects of the errors are illustrated using the 4-prameter Hill model. The results show that when the errors are non-Gaussian, the nonlinear least squares approach produces biased and inefficient estimators. An extension of the well-known simulation-extrapolation (SIMEX) method is developed for the case when the EIV lead to biased model parameters estimators, and is called Berkson simulation-extrapolation (BSIMEX). BSIMEX requires the error variance to be known. The robustness of the adjusted approach for misspecified variance is examined. Moreover, it is shown that BSIMEX performs better than the regression calibration methods when the EIV are dependent, while the regression calibration methods are preferable when the EIV are independent.
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Morse, Brendan J. "Controlling Type 1 errors in moderated multiple regression an application of item response theory for applied psychological research /." Ohio : Ohio University, 2009. http://www.ohiolink.edu/etd/view.cgi?ohiou1247063796.
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Morse, Brendan J. "Controlling Type I Errors in Moderated Multiple Regression: An Application of Item Response Theory for Applied Psychological Research." Ohio University / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1247063796.
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Börsum, Jakob, and Jakob Nyblom. "Prognoser på försäkringsdata : En utvärdering av prediktionsmodeller för antal skador på den svenska försäkringsmarknaden." Thesis, Uppsala universitet, Statistiska institutionen, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-374731.
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The purpose of this report is to predict annual insurance data with quarterly data as predictors and to evaluate its accuracy against other naive prediction models. A relationship is discerned between the two data categories and the interest goes beyond publication frequency as there is a fundamental difference between quarterly and annual data. The insurance industry organization Insurance Sweden publishes quarterly data that contain all insurance events reported while the annual data only contain insurance events which led to disbursement from the insurance companies. This discrepancy shows to be problematic when predicting annual outcomes. Forecasts are estimated by ARIMA models on short time series and compared with classic linear regression models. The implied results from all insurance subcategories in traffic, motor vehicles and household- and corporate insurance are that, in some cases, prediction using linear regression on quarterly data is more precise than the constructed naive prediction models on annual data. However, the results vary between subcategories and the regression models using quarterly data need further improvement before it is the obvious choice when forecasting annual number of events that led to disbursements from the insurance companies.
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Šimek, Jan. "Prognóza vývoje trhu zlata." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2018. http://www.nusl.cz/ntk/nusl-377951.
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The diploma thesis deals with econometric modelling and gold price forecast. A key factor is the multiple regression model and the ARIMA model. The first part of the diploma thesis contains a theoretical basis. The analytical part deals with modelling of gold market price and subsequent forecasting. Statistical and econometric verification using statistical methods play a very important role. The last part summarizes the results and makes suggestions for improvement.
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Karlsson, Andreas. "Estimation and Inference for Quantile Regression of Longitudinal Data : With Applications in Biostatistics." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-7186.
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Engström, Freja, and Rojas Disa Nilsson. "Prediction of the future trend of e-commerce." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301950.
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In recent years more companies have invested in electronic commerce as a result of more customers using the internet as a tool for shopping. However, the basics of marketing still apply to online stores, and thus companies need to conduct market analyses of customers and the online market to be able to successfully target customers online. In this report, we propose the use of machine learning, a tool that has received a lot of attention and positive affirmation for the ability to tackle a range of problems, to predict future trends of electronic commerce in Sweden. More precise, to predict the future share of users of electronic commerce in general and for certain demographics. We will build three different models, polynomial regression, SVR and ARIMA. The findings from the constructed forecasts were that there are differences between different demographics of customers and between groups within a certain demographic. Furthermore, the result showed that the forecast was more accurate when modelling a certain demographic than the entire population. Companies can thereby possibly use the models to predict the behaviour of certain smaller segments of the market and use that in their marketing to attract these customers.<br>Pa senare år har många företag investerat i elektronisk handel, även kallat e-handel, vilket är ett resultat av att individer i samhället i större utsträckning använder internet som ett redskap. Grunderna för marknadsföring gäller fortfarande för webbaserade butiker, och därmed behöver företag genomföra marknadsanalyser över potentiella kunder och internet-marknaden för att kunna lansera starka marknadsföringskampanjer. I denna rapport föreslår vi användning av maskininlärning, ett verktyg som har fått mycket uppmärksamhet på senaste tiden för dess förmåga att hantera olika problem kring data och för att prognostisera framtida trender för e-handel i Sverige. Mer exakt kommer andelen användare av e-handel i framtiden prognostiseras, både generellt och för enskilda demografier. Vi kommer att implementera tre olika modeller, polynomisk regression, SVR och ARIMA. Resultaten från de konstruerade prognoserna visar att det finns tydliga skillnader mellan olika demografier av kunder och mellan grupper inom en viss demografi. Dessutom visade resultaten att prognoserna var mer exakta vid modellering av en viss demografi än över hela befolkningen. Företag kan därmed möjligtvis använda modellerna för att förutsäga beteendet hos vissa mindre segment av marknaden.
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Kuljus, Kristi. "Rank Estimation in Elliptical Models : Estimation of Structured Rank Covariance Matrices and Asymptotics for Heteroscedastic Linear Regression." Doctoral thesis, Uppsala universitet, Matematisk statistik, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-9305.
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This thesis deals with univariate and multivariate rank methods in making statistical inference. It is assumed that the underlying distributions belong to the class of elliptical distributions. The class of elliptical distributions is an extension of the normal distribution and includes distributions with both lighter and heavier tails than the normal distribution. In the first part of the thesis the rank covariance matrices defined via the Oja median are considered. The Oja rank covariance matrix has two important properties: it is affine equivariant and it is proportional to the inverse of the regular covariance matrix. We employ these two properties to study the problem of estimating the rank covariance matrices when they have a certain structure. The second part, which is the main part of the thesis, is devoted to rank estimation in linear regression models with symmetric heteroscedastic errors. We are interested in asymptotic properties of rank estimates. Asymptotic uniform linearity of a linear rank statistic in the case of heteroscedastic variables is proved. The asymptotic uniform linearity property enables to study asymptotic behaviour of rank regression estimates and rank tests. Existing results are generalized and it is shown that the Jaeckel estimate is consistent and asymptotically normally distributed also for heteroscedastic symmetric errors.
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Snow, Kyle Brian. "Topics in Total Least-Squares Adjustment within the Errors-In-Variables Model: Singular Cofactor Matrices and Prior Information." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1354677123.
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Almqvist, Olof. "A comparative study between algorithms for time series forecasting on customer prediction : An investigation into the performance of ARIMA, RNN, LSTM, TCN and HMM." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-16974.
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Time series prediction is one of the main areas of statistics and machine learning. In 2018 the two new algorithms higher order hidden Markov model and temporal convolutional network were proposed and emerged as challengers to the more traditional recurrent neural network and long-short term memory network as well as the autoregressive integrated moving average (ARIMA). In this study most major algorithms together with recent innovations for time series forecasting is trained and evaluated on two datasets from the theme park industry with the aim of predicting future number of visitors. To develop models, Python libraries Keras and Statsmodels were used. Results from this thesis show that the neural network models are slightly better than ARIMA and the hidden Markov model, and that the temporal convolutional network do not perform significantly better than the recurrent or long-short term memory networks although having the lowest prediction error on one of the datasets. Interestingly, the Markov model performed worse than all neural network models even when using no independent variables.
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Bajracharya, Dinesh. "Econometric Modeling vs Artificial Neural Networks : A Sales Forecasting Comparison." Thesis, Högskolan i Borås, Institutionen Handels- och IT-högskolan, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-20400.
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Econometric and predictive modeling techniques are two popular forecasting techniques. Both ofthese techniques have their own advantages and disadvantages. In this thesis some econometricmodels are considered and compared to predictive models using sales data for five products fromICA a Swedish retail wholesaler. The econometric models considered are regression model,exponential smoothing, and ARIMA model. The predictive models considered are artificialneural network (ANN) and ensemble of neural networks. Evaluation metrics used for thecomparison are: MAPE, WMAPE, MAE, RMSE, and linear correlation. The result of this thesisshows that artificial neural network is more accurate in forecasting sales of product. But it doesnot differ too much from linear regression in terms of accuracy. Therefore the linear regressionmodel which has the advantage of being comprehensible can be used as an alternative to artificialneural network. The results also show that the use of several metrics contribute in evaluatingmodels for forecasting sales.<br>Program: Magisterutbildning i informatik
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Mbiti, Titus Kivaa Peter, and tkivaap@yahoo com. "A System Dynamics Model of Construction Output in Kenya." RMIT University. Property Construction & Project Management, 2008. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20081211.160910.
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This study investigates fluctuations of construction output, and growth of the output in Kenya. Fluctuation and growth of construction activity are matters of concern in construction industries of many countries in the developing as well as in the developed world. The construction industry of Kenya is therefore an exemplifying case for this phenomenon. Construction activity in Kenya fluctuates excessively and grows very slowly. This remains a big challenge to policy makers, developers, consultants and contractors in their decision-making processes. In this study, systems thinking was applied to investigate the problem of excessive fluctuations and stunted growth of construction output in Kenya. The study developed a system dynamics model to simulate the construction output problem behaviour. The historical behaviour of the construction industry was described using construction output data of a 40-year period - from 1964 to 2003. Line graphs of the historical data exhibited profiles that helped to identify the system archetypes operating in the industry. From the profiles, it was deduced that the problem of fluctuations and slow growth of construction output in Kenya is encapsulated in two system archetypes, namely: balancing process with a delay, and limits to growth. The relationship between construction output and its determinant factors from the constru ction industry's environment was investigated using time series regression, which involved autoregressive integrated moving average (ARIMA) regression and multiple regression modelling of the output. On the basis of the historical data analysis and the system archetypes identified, a system dynamics (SD) model was developed to replicate the problem of fluctuations and slow growth in the construction output. The data used to develop the system dynamics model was annual construction output in Kenya from 1964 to 2003. The model was then used: to appraise policy changes suggested by construction industry participants in Kenya, and to project construction output in Kenya from year 2004 to year 2050, in order to establish the expected future fluctuations and growth trends of the construction output. It was observed that three fundamental changes are necessary in the system structure of the construction industry of Kenya, in order to minimize fluctuations and foster growth in construction output in the country, in the long run. The changes are: setting long-term targets of annual construction output in the industry as a whole, incorporating reserve capacity in the production process, and expanding the system st ructure to capture a larger construction market. The study recommends regulation of the response of the construction industry of Kenya to changes in construction demand in the market, and expansion of the construction industry's market into the African region and beyond.
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Ramos, Anthony Kojo. "Forecasting Mortality Rates using the Weighted Hyndman-Ullah Method." Thesis, Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-54711.
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The performance of three methods of mortality modelling and forecasting are compared. These include the basic Lee–Carter and two functional demographic models; the basic Hyndman–Ullah and the weighted Hyndman–Ullah. Using age-specific data from the Human Mortality Database of two developed countries, France and the UK (England&Wales), these methods are compared; through within-sample forecasting for the years 1999-2018. The weighted Hyndman–Ullah method is adjudged superior among the three methods through a comparison of mean forecast errors and qualitative inspection per the dataset of the selected countries. The weighted HU method is then used to conduct a 32–year ahead forecast to the year 2050.
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Yue, Xiaohui. "Detecting Rater Centrality Effect Using Simulation Methods and Rasch Measurement Analysis." Diss., Virginia Tech, 2011. http://hdl.handle.net/10919/28423.
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This dissertation illustrates how to detect the rater centrality effect in a simulation study that approximates data collected in large scale performance assessment settings. It addresses three research questions that: (1) which of several centrality-detection indices are most sensitive to the difference between effect raters and non-effect raters; (2) how accurate (and inaccurate), in terms of Type I error rate and statistical power, each centrality-detection index is in flagging effect raters; and (3) how the features of the data collection design (i.e., the independent variables including the level of centrality strength, the double-scoring rate, and the number of raters and ratees) influence the accuracy of rater classifications by these centrality-detection indices. The results reveal that the measure-residual correlation, the expected-residual correlation, and the standardized deviation of assigned scores perform better than the point-measure correlation. The mean-square fit statistics, traditionally viewed as potential indicators of rater centrality, perform poorly in terms of differentiating central raters from normal raters. Along with the rater slope index, the mean-square fit statistics did not appear to be sensitive to the rater centrality effect. All of these indices provided reasonable protection against Type I errors when all responses were double scored, and that higher statistical power was achieved when responses were 100% double scored in comparison to only 10% being double scored. With a consideration on balancing both Type I error and statistical power, I recommend the measure-residual correlation and the expected-residual correlation for detecting the centrality effect. I suggest using the point-measure correlation only when responses are 100% double scored. The four parameters evaluated in the experimental simulations had different impact on the accuracy of rater classification. The results show that improving the classification accuracy for non-effect raters may come at a cost of reducing the classification accuracy for effect raters. Some simple guidelines for the expected impact of classification accuracy when a higher-order interaction exists summarized from the analyses offer a glimpse of the â prosâ and â consâ in adjusting the magnitude of the parameters when we evaluate the impact of the four experimental parameters on the outcomes of rater classification.<br>Ph. D.
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Rodrigues, Stephanie Nancy da Veiga Jassy Barbosa. "Modelação e previsão da despesa em cuidados de saúde em Portugal." Master's thesis, Instituto Superior de Economia e Gestão, 2016. http://hdl.handle.net/10400.5/13147.
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Mestrado em Decisão Económica e Empresarial<br>Esta tese teve como objectivo a modelação e previsão em cuidados de saúde (privada, pública e total) recorrendo ao modelo ARIMA. Foi realizado um estudo econométrico sobre as variáveis potencialmente explicativas da evolução da despesa em cuidados de saúde em Portugal recorrendo ao modelo de Regressão Linear Múltipla. Para realizar a modelização e previsão das despesas em cuidados de saúde bem como para o estudo econométrico das despesas em cuidados de saúde, foi utilizado o software Eviews 9.0. O modelo ARIMA (2,1,2) de acordo com os critérios AIC e BIC mostrou ser o melhor modelo para previsões para as despesas em cuidados de saúde (privada, pública e total) em Portugal nos anos 2014 e 2015. O resultado das previsões para as despesas em cuidados de saúde (privada, pública e total) indica que se irá verificar um aumento. Este resultado indicou ser bastante positivo dado que a diferença entre o valor previsto e o valor real da despesa em cuidados de saúde em 2015 foi pequena.
A mortalidade infantil, proxy da evolução tecnológica segundo Okunate & Murthy (2002) mostrou ser a variável com maior capacidade de explicação da variação na despesa em cuidados de saúde (privada, pública e total) em Portugal. Assim, a mortalidade parece ser uma boa proxy dos efeitos do progresso tecnológico embora Newhose (1992), Okunate & Murthy (2002) afirmarem que é necessário realizar investigações sobre a conceptualização, mensuração e a incorporação destes efeitos para a explicação da evolução da despesa em cuidados de saúde.<br>This theses had the purpose of modeling and forecast the healthcare expenses (private, public and total), using the ARIMA model. it was made an econometric study of the potentially explanatory variables of expenditure developments in health care in Portugal using the Multiple Linear Regression Model. To perform the modeling and forecasting of costs in health care and the econometric study of costs in health care, it was used the Eviews 9.0 software.
The ARIMA (2,1,2) according to the AIC and BIC criteria was the best model to make forecasts for spending on private, public and total health care in Portugal in the years 2014 and 2015. The result of the forecasts for spending on private, public and total health care demonstrated that we will see an increase. This result indicated to be very positive as the difference between the predicted value and the real value of expenditure on health care in 2015 was small.
Infant mortality, proxy of technological according with Okunate & Murthy (2002) proved to be the variable with the greatest capacity for the explanation on the variation of the spending on private health care, public and total health care in Portugal. Thus, mortality seems to be a good proxy of the effects of technological progress but Newhose (1992), Okunate & Murthy (2002) claim that it is necessary to carry out investigations regarding the conceptualization, measurement, and the incorporation of these effects to explain the evolution of expenditure in health care.<br>info:eu-repo/semantics/publishedVersion
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Rey, Diana. "A Gasoline Demand Model for the United States Light Vehicle Fleet." Master's thesis, University of Central Florida, 2009. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2351.
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The United States is the world's largest oil consumer demanding about twenty five percent of the total world oil production. Whenever there are difficulties to supply the increasing quantities of oil demanded by the market, the price of oil escalates leading to what is known as oil price spikes or oil price shocks. The last oil price shock which was the longest sustained oil price run up in history, began its course in year 2004, and ended in 2008. This last oil price shock initiated recognizable changes in transportation dynamics: transit operators realized that commuters switched to transit as a way to save gasoline costs, consumers began to search the market for more efficient vehicles leading car manufactures to close 'gas guzzlers' plants, and the government enacted a new law entitled the Energy Independence Act of 2007, which called for the progressive improvement of the fuel efficiency indicator of the light vehicle fleet up to 35 miles per gallon in year 2020. The past trend of gasoline consumption will probably change; so in the context of the problem a gasoline consumption model was developed in this thesis to ascertain how some of the changes will impact future gasoline demand. Gasoline demand was expressed in oil equivalent million barrels per day, in a two steps Ordinary Least Square (OLS) explanatory variable model. In the first step, vehicle miles traveled expressed in trillion vehicle miles was regressed on the independent variables: vehicles expressed in million vehicles, and price of oil expressed in dollars per barrel. In the second step, the fuel consumption in million barrels per day was regressed on vehicle miles traveled, and on the fuel efficiency indicator expressed in miles per gallon. The explanatory model was run in EVIEWS that allows checking for normality, heteroskedasticty, and serial correlation. Serial correlation was addressed by inclusion of autoregressive or moving average error correction terms. Multicollinearity was solved by first differencing. The 36 year sample series set (1970-2006) was divided into a 30 years sub-period for calibration and a 6 year "hold-out" sub-period for validation. The Root Mean Square Error or RMSE criterion was adopted to select the "best model" among other possible choices, although other criteria were also recorded. Three scenarios for the size of the light vehicle fleet in a forecasting period up to 2020 were created. These scenarios were equivalent to growth rates of 2.1, 1.28, and about 1 per cent per year. The last or more optimistic vehicle growth scenario, from the gasoline consumption perspective, appeared consistent with the theory of vehicle saturation. One scenario for the average miles per gallon indicator was created for each one of the size of fleet indicators by distributing the fleet every year assuming a 7 percent replacement rate. Three scenarios for the price of oil were also created: the first one used the average price of oil in the sample since 1970, the second was obtained by extending the price trend by exponential smoothing, and the third one used a longtime forecast supplied by the Energy Information Administration. The three scenarios created for the price of oil covered a range between a low of about 42 dollars per barrel to highs in the low 100's. The 1970-2006 gasoline consumption trend was extended to year 2020 by ARIMA Box-Jenkins time series analysis, leading to a gasoline consumption value of about 10 millions barrels per day in year 2020. This trend line was taken as the reference or baseline of gasoline consumption. The savings that resulted by application of the explanatory variable OLS model were measured against such a baseline of gasoline consumption. Even on the most pessimistic scenario the savings obtained by the progressive improvement of the fuel efficiency indicator seem enough to offset the increase in consumption that otherwise would have occurred by extension of the trend, leaving consumption at the 2006 levels or about 9 million barrels per day. The most optimistic scenario led to savings up to about 2 million barrels per day below the 2006 level or about 3 millions barrels per day below the baseline in 2020. The "expected" or average consumption in 2020 is about 8 million barrels per day, 2 million barrels below the baseline or 1 million below the 2006 consumption level. More savings are possible if technologies such as plug-in hybrids that have been already implemented in other countries take over soon, are efficiently promoted, or are given incentives or subsidies such as tax credits. The savings in gasoline consumption may in the future contribute to stabilize the price of oil as worldwide demand is tamed by oil saving policy changes implemented in the United States.<br>M.S.<br>Department of Civil and Environmental Engineering<br>Engineering and Computer Science<br>Civil Engineering MS
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Hassanein, Ahmed. "Informativeness of unaudited forward-looking financial disclosure : evidence from UK narrative reporting." Thesis, University of Plymouth, 2015. http://hdl.handle.net/10026.1/5143.
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Forward-looking financial disclosure (FLFD) is potentially uninformative if it does not change from the previous year, especially after a significant change in firm performance. This study uses a sample of UK narrative statements of the annual reports over the period from 2005 to 2011. It employed the automated content analysis technique to measure change in FLFD over years to answer three research questions. First, to what extent does change in firms’ earnings performance drive managers to change FLFD over years? Second, what are the other drivers of the change of FLFD from year to year? Third, do investors use information revealed by the change in FLFD? The study finds a positive association between change in FLFD and change in firm earnings performance. However, it finds weak evidence that firms with larger changes in their earnings performance are likely to change their FLFD more than those with smaller performance changes. In addition, when we distinguish between well-performing and poorly performing firms, it finds that the change in FLFD is more positively associated with poorly performing firms compared to well-performing firms. Furthermore, it finds that change in FLFD is positively (negatively) associated with firm size, (competitive environment), (litigious environment), and (percentage of managerial ownership). In addition, the role of the auditor in overseeing narrative reporting is not appearing for all sample firms or for well-performing firms, however, it is observable only in poorly performing firms. Finally, the study uses firm value three months after the release of the annual report to examine investors’ responses to the changes in FLFD. It finds that the value of a firm decreases as long as it changes its FLFD from the previous year. However, when we distinguish between well and poorly performing firms, it finds that the change in FLFD has no effect on the value of well-performing firms, while, it negatively affects poorly performing firms. The results suggest that FLFD in UK narratives includes some content about firm performance. However, it neither affects the value of well-performing firms nor enhances investors’ valuation of poorly performing firms.
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Fabio, Lizandra Castilho. "Erros não detectáveis no processo de estimação de estado em sistemas elétricos de potência." Universidade de São Paulo, 2006. http://www.teses.usp.br/teses/disponiveis/18/18133/tde-01032016-120822/.
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Na tentativa de contornar os problemas ainda existentes para a detecção e identificação de erros grosseiros (EGs) no processo de estimação de estado em sistemas elétricos de potência (EESEP), realiza-se, neste trabalho, uma análise da formulação dos estimadores aplicados a sistemas elétricos de potência, em especial, o de mínimos quadrados ponderados, tendo em vista evidenciar as limitações dos mesmos para o tratamento de EGs. Em razão da dificuldade de detectar EGs em medidas pontos de alavancamento, foram também analisadas as metodologias desenvolvidas para identificação de medidas pontos de alavancamento. Através da formulação do processo de EESEP como um problema de álgebra linear, demonstra-se o porquê da impossibilidade de detectar EGs em determinadas medidas redundantes, sendo proposto, na seqüência, um método para identificação de medidas pontos de alavancamento. Para reduzir os efeitos maléficos dessas medidas no processo de EESEP verifica-se a possibilidade de aplicar outras técnicas estatísticas para o processamento de EGs, bem como técnicas para obtenção de uma matriz de ponderação adequada.<br>To overcome the problems still existent for gross errors (GEs) detection and identification in the process of power system state estimation (PSSE), the formulations of the estimators applied to power systems are analyzed, specially, the formulation of the weighted squares estimator. These analyses were performed to show the limitations of these estimators for GEs processing. As leverage points (LP) represent a problem for GEs processing, methodologies for LP identification were also verified. By means of the linear formulation of the PSSE process, the reason for the impossibility of GEs detection in some redundant measurements is shown and a method for LP identification is proposed. To minimize the bad effects of the LP to the PSSE process, the possibility of applying other statistic techniques for GEs processing, as well as techniques to estimate an weighting matrix are also analyzed.
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Stavrén, Fredrik, and Nikita Domin. "Modeling of non-maturing deposits." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252302.
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The interest in modeling non-maturing deposits has skyrocketed ever since thefinancial crisis 2008. Not only from a regulatory and legislative perspective,but also from an investment and funding perspective.Modeling of non-maturing deposits is a very broad subject. In this thesis someof the topics within the subject are investigated, where the greatest focus inon the modeling of the deposit volumes. The main objective is to providethe bank with an analysis of the majority of the topics that needs to be cov-ered when modeling non-maturing deposits. This includes short-rate model-ing using Vasicek’s model, deposit rate modeling using a regression approachand a method proposed by Jarrow and Van Deventer, volume modeling usingSARIMA, SARIMAX and a general additive model, a static replicating port-folio based on Maes and Timmerman’s to model the behaviour of the depositaccounts and finally a liquidity risk model that was suggested by Kalkbrenerand Willing. All of these models have been applied on three different accounttypes: private transaction accounts, savings accounts and corporate savingsaccounts.The results are that, due to the current market, the static replicating portfoliodoes not achieve the desired results. Furthermore, the best volume model forthe data provided is a SARIMA model, meaning the effect of the exogenousvariables are seemingly already embedded in the lagged volume. Finally, theliquidity risk results are plausible and thus deemed satisfactory.<br>Intresset för att modellera inlåningsvolymer utan en kontrakterad förfallodaghar ökat markant sedan finanskrisen 2008. Inte bara sett utifrån ett perspek-tiv att uppfylla krav som ställs av tillsynsmyndigheter, men också sett utifrånbankens investerings-och finansieringsperspektiv.Målet med det här arbetet är att förse banken med en analys av majoritetenav de olika områdena som man behöver ta hänsyn till när man ska model-lera inlåningar utan förfallodatum, men med ett fokus på volymmodellering.I den här rapporten modelleras räntor (kortränta och kontoränta), kontovoly-merna, kontobeteendet samt likviditetsrisken. Detta görs med hjälp av Vasicekför korträntan, en regressionsmetod samt en metod som föreslagits av Jarrowoch Van Deventer för kontoräntan, SARIMA, SARIMAX och en generell ad-ditiv regressionsmetod för volymerna, en statisk replikeringsportfölj baseradpå Maes och Timmermans modell för att imitera kontona och slutligen så mo-delleras likviditetsrisken med ett ramverk som föreslagits av Kalkbrener ochWilling. Alla dessa nämnda modeller appliceras, där det är möjligt, på de treolika kontotyperna: privatkonton, sparkonton samt företagssparkonto.Resultatet är att räntemodelleringen samt replikeringsportföljen inte ger ade-kvata resultat på grund av den rådande marknaden. Vidare så ger en SARIMA-modell den bästa prediktionen, vilket gör att slutsatsen är att andra exogenavariabler redan är inneslutna i den fördröjda volymvariabeln. Avslutningsvisså ger likviditetsmodellen tillfredsställande resultat och antas vara rimlig.
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Norng, Sorn. "Statistical decisions in optimising grain yield." Queensland University of Technology, 2004. http://eprints.qut.edu.au/15806/.
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This thesis concerns Precision Agriculture (PA) technology which involves methods developed to optimise grain yield by examining data quality and modelling protein/yield relationship of wheat and sorghum fields in central and southern Queensland. An important part of developing strategies to optimisise grain yield is the understanding of PA technology. This covers major aspects of PA which includes all the components of Site- Specific Crop Management System (SSCM). These components are 1. Spatial referencing, 2. Crop, soil and climate monitoring, 3. Attribute mapping, 4. Decision suppport systems and 5. Differential action. Understanding how all five components fit into PA significantly aids the development of data analysis methods. The development of PA is dependent on the collection, analysis and interpretation of information. A preliminary data analysis step is described which covers both non-spatial and spatial data analysis methods. The non-spatial analysis involves plotting methods (maps, histograms), standard distribution and statistical summary (mean, standard deviation). The spatial analysis covers both undirected and directional variogram analyses. In addition to the data analysis, a theoretical investigation into GPS error is given. GPS plays a major role in the development of PA. A number of sources of errors affect the GPS and therefore effect the positioning measurements. Therefore, an understanding of the distribution of the errors and how they are related to each other over time is needed to complement the understanding of the nature of the data. Understanding the error distribution and the data give useful insights for model assumptions in regard to position measurement errors. A review of filtering methods is given and new methods are developed, namely, strip analysis and a double harvesting algoritm. These methods are designed specifically for controlled traffic and normal traffic respectively but can be applied to all kinds of yield monitoring data. The data resulting from the strip analysis and double harvesting algorithm are used in investigating the relationship between on-the-go yield and protein. The strategy is to use protein and yield in determining decisions with respect to nitrogen managements. The agronomic assumption is that protein and yield have a significant relationship based on plot trials. We investigate whether there is any significant relationship between protein and yield at the local level to warrent this kind of assumption. Understanding PA technology and being aware of the sources of errors that exist in data collection and data analysis are all very important in the steps of developing management decision strategies.
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Favari, Daniel Fernando de. ""Uma aplicação industrial de regressão binária com erros na variável explicativa"." Universidade de São Paulo, 2006. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-02082006-153701/.
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Neste trabalho, aplicamos um modelo de regressão binária com erros de medição na variável explicativa para analisar sistemas de medição do tipo atributo. Para isto, utilizamos o modelo logístico com erros na variável, para o qual obtemos as estimativas de máxima verossimilhança via o algoritmo EM e a matriz de informação de Fisher observada. Além disso, fizemos um estudo de simulação para compararmos o método analítico e os modelos logístico sem erros na variável (ingênuo) e logístico com erros na variável. Finalmente, aplicamos nossa metodologia para avaliarmos um sistema de medição passa/não passa da maior montadora de motores Diesel (MWM International).<br>In this work, we apply a study of binary regression model with errors-in-variable to analyze attributive measurement systems. For this, we use the logistic model with errors-in-variable to obtain parameter estimates of maximum likelihood through EM algorithm and the observed Fisher information matrix. In addition we do a simulation study to compare analytic method and the logistic model with and without measurement errors-in-variable. Finally, we apply our methodology to evaluate a attributive measurement system for the largest Diesel motor company of the world (MWM International).
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Willersjö, Nyfelt Emil. "Comparison of the 1st and 2nd order Lee–Carter methods with the robust Hyndman–Ullah method for fitting and forecasting mortality rates." Thesis, Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-48383.
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The 1st and 2nd order Lee–Carter methods were compared with the Hyndman–Ullah method in regards to goodness of fit and forecasting ability of mortality rates. Swedish population data was used from the Human Mortality Database. The robust estimation property of the Hyndman–Ullah method was also tested with inclusion of the Spanish flu and a hypothetical scenario of the COVID-19 pandemic. After having presented the three methods and making several comparisons between the methods, it is concluded that the Hyndman–Ullah method is overall superior among the three methods with the implementation of the chosen dataset. Its robust estimation of mortality shocks could also be confirmed.
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Porto, Rogério de Faria. "Regressão não-paramétrica com erros correlacionados via ondaletas." Universidade de São Paulo, 2008. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-27102008-101711/.
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Nesta tese, são obtidas taxas de convergência a zero, do risco de estimação obtido com regressão não-paramétrica via ondaletas, quando há erros correlacionados. Quatro métodos de regressão não-paramétrica via ondaletas, com delineamento desigualmente espaçado são estudados na presença de erros correlacionados, oriundos de processos estocásticos. São apresentadas condições sobre os erros e adaptações aos procedimentos necessárias à obtenção de taxas de convergência quase minimax, para os estimadores. Sempre que possível são obtidas taxas de convergência para os estimadores no domínio da função, sob condições bastante gerais a respeito da função a ser estimada, do delineamento e da correlação dos erros. Mediante estudos de simulação, são avaliados os comportamentos de alguns métodos propostos quando aplicados a amostras finitas. Em geral sugere-se usar um dos procedimentos estudados, porém aplicando-se limiares por níveis. Como a estimação da variância dos coecientes de detalhes pode ser problemática em alguns casos, também se propõe um procedimento iterativo semi-paramétrico geral para métodos que utilizam ondaletas, na presença de erros em séries temporais.<br>In this thesis, rates of convergence to zero are obtained for the estimation risk, for non-parametric regression using wavelets, when the errors are correlated. Four non-parametric regression methods using wavelets, with un-equally spaced design are studied in the presence of correlated errors, that come from stochastic processes. Conditions on the errors and adaptations to the procedures are presented, so that the estimators achieve quasi-minimax rates of convergence. Whenever is possible, rates of convergence are obtained for the estimators in the domain of the function, under mild conditions on the function to be estimated, on the design and on the error correlation. Through simulation studies, the behavior of some of the proposed methods is evaluated, when used on finite samples. Generally, it is suggested to use one of the studied methods, however applying thresholds by level. Since the estimation of the detail coecients can be dicult in some cases, it is also proposed a general semi-parametric iterative procedure, for wavelet methods in the presence of time-series errors.
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Souza, Aline Campos Reis de. "Modelos de regressão linear heteroscedásticos com erros t-Student : uma abordagem bayesiana objetiva." Universidade Federal de São Carlos, 2016. https://repositorio.ufscar.br/handle/ufscar/7540.
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Previous issue date: 2016-02-18<br>Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)<br>In this work , we present an extension of the objective bayesian analysis made in Fonseca et al. (2008), based on Je reys priors for linear regression models with Student
t errors, for which we consider the heteroscedasticity assumption. We show that the
posterior distribution generated by the proposed Je reys prior, is proper. Through
simulation study , we analyzed the frequentist properties of the bayesian estimators
obtained. Then we tested the robustness of the model through disturbances in the response variable by comparing its performance with those obtained under another prior distributions proposed in the literature. Finally, a real data set is used to analyze the performance of the proposed model . We detected possible in uential points through
the Kullback -Leibler divergence measure, and used the selection model criterias EAIC,
EBIC, DIC and LPML in order to compare the models.<br>Neste trabalho, apresentamos uma extensão da análise bayesiana objetiva feita em Fonseca et al. (2008), baseada nas distribuicões a priori de Je reys para o modelo de regressão linear com erros t-Student, para os quais consideramos a suposicão de heteoscedasticidade. Mostramos que a distribuiçãoo a posteriori dos parâmetros do modelo regressão gerada pela distribuição a priori e própria. Através de um estudo de simulação, avaliamos as propriedades frequentistas dos estimadores bayesianos e comparamos os resultados com outras
distribuições a priori encontradas na literatura. Além disso, uma análise de diagnóstico baseada na medida de divergência Kullback-Leiber e desenvolvida com analidade de estudar a robustez das estimativas na
presença de observações atípicas. Finalmente, um conjunto de dados reais e utilizado para o ajuste do modelo proposto.
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Blanco, Felipe Orestes Cuan Suárez. "Influences of external factors in automotive sales forecasting : a portuguese case study." Master's thesis, 2018. http://hdl.handle.net/10362/40987.
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Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence<br>This study aims to contribute to a better understanding of the Portuguese automotive market, its
behaviour and, additionally, attempt to create better sales prediction models, using linear regression
models with ARIMA errors.
Through time series analysis, it will be studied how external factors such as socio-economic variables,
consumer behaviour as well as market specific factors (e.g. gas prices, car rental services) affects the
development of the automotive market. Furthermore, it will be tested if methodologies such as
hierarchical forecasting on a bottom-up approach will bring better results compared to forecasting
the market as a whole.
This understanding and forecasting techniques, if proven successful, can bring higher competitive
advantage to companies in this sector, through more accurate planning and strategic management.
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Shin, Yoon Sung. "Three Essays on Energy Economics and Forecasting." Thesis, 2011. http://hdl.handle.net/1969.1/ETD-TAMU-2011-12-10689.
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This dissertation contains three independent essays relating energy economics. The first essay investigates price asymmetry of diesel in South Korea by using the error correction model. Analyzing weekly market prices in the pass-through of crude oil, this model shows asymmetric price response does not exist at the upstream market but at the downstream market. Since time-variant residuals are found by the specified models for both weekly and daily retail prices at the downstream level, these models are implemented by a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) process. The estimated results reveal that retail prices increase fast in the rise of crude oil prices but decrease slowly in the fall of those. Surprisingly, retail prices rarely respond to changes of crude oil prices for the first five days. Based on collusive behaviors of retailers, this price asymmetry in Korea diesel market is explained.
The second essay aims to evaluate the new incentive system for biodiesel in South Korea, which keeps the blend mandate but abolishes tax credits for government revenues. To estimate changed welfare from the new policy, a multivariate stochastic simulation method is applied into time-series data for the last five years. From the simulation results, the new biodiesel policy will lead government revenues to increases with the abolishment of tax credit. However, increased prices of blended diesel will cause to decrease demands of both biodiesel and blended diesel, so consumer and producer surplus in the transport fuel market will decrease.
In the third essay, the Regression - Seasonal Autoregressive Integrated Moving Average (REGSARIMA) model is employed to predict the impact of air temperature on daily peak load demand in Houston. Compared with ARIMA and Seasonal Model, a REGARIMA model provides the more accurate prediction for daily peak load demand for the short term. The estimated results reveal air temperature in the Houston areas causes an increase in electricity consumption for cooling but to save that for heating. Since the daily peak electricity consumption is significantly affected by hot air temperature, this study makes a conclusion that it is necessary to establish policies to reduce urban heat island phenomena in Houston.