Academic literature on the topic 'Ordinary least square model'
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Journal articles on the topic "Ordinary least square model"
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Park, Min Jung, and Soon Man Kwon. "Socioeconomic Determinants of Korean Medicine Ambulatory Services: Comparing Panel Fixed Effect Model with Pooled Ordinary Least Square." Health Policy and Management 24, no. 1 (March 31, 2014): 47–55. http://dx.doi.org/10.4332/kjhpa.2014.24.1.47.
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Watagoda, Lasanthi C. R. Pelawa. "A Sub-Model Theorem for Ordinary Least Squares." International Journal of Statistics and Probability 8, no. 1 (November 19, 2018): 40. http://dx.doi.org/10.5539/ijsp.v8n1p40.
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Variable selection or subset selection is an important step in the process of model fitting. There are many ways to select the best subset of variables including Forward selection, Backward elimination, etcetera. Ordinary least squares (OLS) is one of the most commonly used methods of fitting the final model. Final sub-model can perform poorly if the variable selection process failed to choose the right number of variables. This paper gives a new theorem and a mathematical proof to illustrate the reason for the poor performances, when using the least squares method after variable selection.
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Yeniay, Özgür, Öznur İşçi, Atilla Göktaş, and M. Niyazi Çankaya. "Time Scale in Least Square Method." Abstract and Applied Analysis 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/354237.
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Study of dynamic equations in time scale is a new area in mathematics. Time scale tries to build a bridge between real numbers and integers. Two derivatives in time scale have been introduced and called as delta and nabla derivative. Delta derivative concept is defined as forward direction, and nabla derivative concept is defined as backward direction. Within the scope of this study, we consider the method of obtaining parameters of regression equation of integer values through time scale. Therefore, we implemented least squares method according to derivative definition of time scale and obtained coefficients related to the model. Here, there exist two coefficients originating from forward and backward jump operators relevant to the same model, which are different from each other. Occurrence of such a situation is equal to total number of values of vertical deviation between regression equations and observation values of forward and backward jump operators divided by two. We also estimated coefficients for the model using ordinary least squares method. As a result, we made an introduction to least squares method on time scale. We think that time scale theory would be a new vision in least square especially when assumptions of linear regression are violated.
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Miessi Sanches, Fabio A., Daniel Junior Silva, and Sorawoot Srisuma. "ORDINARY LEAST SQUARES ESTIMATION OF A DYNAMIC GAME MODEL." International Economic Review 57, no. 2 (April 28, 2016): 623–34. http://dx.doi.org/10.1111/iere.12170.
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Yang, Hong Ying, Shuang Lei Feng, Bo Wang, Wei Sheng Wang, and Chun Liu. "Hybrid Corrected Approach for Wind Power Forecasting Based on Ordinary Least Square Method." Advanced Materials Research 846-847 (November 2013): 1392–97. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.1392.
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This paper shows an application of Ordinary Least Square (OLS) in the wind power forecasting field. The OLS algorithm is applied to obtain the estimated parameter of the hybrid correction model, and then the properly structured correction model was used to correct the forecasting errors form the physical forecasting method and the statistical forecasting method. Satisfactory experimental results are obtained for day-ahead forecast by using actual wind power data.
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Yanuar, Ferra. "The Simulation Study to Test the Performance of Quantile Regression Method With Heteroscedastic Error Variance." CAUCHY 5, no. 1 (November 30, 2017): 36. http://dx.doi.org/10.18860/ca.v5i1.4209.
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<div><p class="Keywords">The purpose of this article was to describe the ability of the quantile regression method in overcoming the violation of classical assumptions. The classical assumptions that are violated in this study are variations of non-homogeneous error or heteroscedasticity. To achieve this goal, the simulated data generated with the design of certain data distribution. This study did a comparison between the models resulting from the use of the ordinary least squares and the quantile regression method to the same simulated data. Consistency of both methods was compared with conducting simulation studies as well. This study proved that the quantile regression method had standard error, confidence interval width and mean square error (MSE) value smaller than the ordinary least squares method. Thus it can be concluded that the quantile regression method is able to solve the problem of heteroscedasticity and produce better model than the ordinary least squares. In addition the ordinary least squares is not able to solve the problem of heteroscedasticity.</p></div>
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Lin, Yanli, Guannan Chu, Caiyuan Lin, and Yongda Yan. "An optimized constitutive model for reproducing flow stress–strain relationships of anisotropic materials." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 233, no. 4 (May 22, 2018): 1357–68. http://dx.doi.org/10.1177/0954406218771100.
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Due to the strong anisotropic property of the advanced metal materials used in automobile, aviation, and aerospace, experimental flow stress–strain relations including different stress states are necessary to provide the information of anisotropic hardening and plastic flow for constructing a constitutive model. Therefore, reasonably reproducing the experimental stress–strain relations is the most fundamental work to substitute adequate flow stress–strain curves into the constitutive equation at the same time. However, accurate and stable regression results are difficult to obtain through the current regression models such as power exponent, second-order function model, fourth-order function model, and so forth. In this paper, an optimized model named as a least square quadratic regression model (ordinary least square model) was proposed based on the most useful second-order function model. The significant difference is that all experimental points are used to reproduce the experimental stress–strain relations in ordinary least square model in place of only three experimental points adopted in second-order function model, which results in good regression accuracy. Through comparison, it is found that the regression results by power function are poor with regard to some experimental results, and the results reproduced by second-order function model or fourth-order function model are very sensitive to the experimental points selected to do the regression. The sum of squares for error (SSE) increases sharply when the selected points are unreasonable. In addition, for second-order function and fourth-order function models, only limited experimental points are adopted to do the regression, the best regression accuracy cannot be obtained even if the selected points are reasonable. In contrast, SSE of the regression curve by ordinary least square model reduces to less than 50% of the best regressed result by second-order function model, the yielding behavior and variable strain increment ratio of the anisotropic materials can be reflected more accurately. This is very important for accurately describing the plastic flow behaviors of anisotropic materials.
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Yang, Y., G. Su, Y. Li, F. Liu, and Z. Lin. "A LEAST SQUARE ALGORITHM FOR GEOMETRIC MATCHING OF REMOTE SENSED IMAGES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2020 (August 3, 2020): 121–25. http://dx.doi.org/10.5194/isprs-annals-v-2-2020-121-2020.
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Abstract. The aim of geometric matching is to extract the geometric transformation parameters between the corresponding images. That is useful for photogrammetric mapping, deformation detection, and flying platform's posture analyses, etc. It is different compare with ordinary feature based image matching succeed by selecting feature points correctly, the proposed method takes all the pixels within the corresponding images to participate the matching procedure for calculating the geometric parameters by least square criterion. The principle of the algorithm, such as the gray corresponding equation, the information quantity inequation and procedure of least square solution are introduced in detail. Particularly, the wavelet analyses for gray signal and calculating the information quantity by signal to noise ratio. Finally, a series of sequential images obtained by a low-altitude helicopter equipped with a video camera was used to test and verify the validity and reliability of the theory and algorithm in this paper. Two typical results are got according to the relative orientation elements model and parallax grid model. The conclusion is got in comparing APM with ordinary feature point method by the information quantity inequation.
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Nisak, Siti Choirun. "Seemingly Unrelated Regression Approach for GSTARIMA Model to Forecast Rain Fall Data in Malang Southern Region Districts." CAUCHY 4, no. 2 (June 3, 2016): 57. http://dx.doi.org/10.18860/ca.v4i2.3488.
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Time series forecasting models can be used to predict phenomena that occur in nature. Generalized Space Time Autoregressive (GSTAR) is one of time series model used to forecast the data consisting the elements of time and space. This model is limited to the stationary and non-seasonal data. Generalized Space Time Autoregressive Integrated Moving Average (GSTARIMA) is GSTAR development model that accommodates the non-stationary and seasonal data. Ordinary Least Squares (OLS) is method used to estimate parameter of GSTARIMA model. Estimation parameter of GSTARIMA model using OLS will not produce efficiently estimator if there is an error correlation between spaces. Ordinary Least Square (OLS) assumes the variance-covariance matrix has a constant error 𝜀𝑖𝑗~𝑁𝐼𝐷(𝟎,𝝈𝟐) but in fact, the observatory spaces are correlated so that variance-covariance matrix of the error is not constant. Therefore, Seemingly Unrelated Regression (SUR) approach is used to accommodate the weakness of the OLS. SUR assumption is 𝜀𝑖𝑗~𝑁𝐼𝐷(𝟎,𝚺) for estimating parameters GSTARIMA model. The method to estimate parameter of SUR is Generalized Least Square (GLS). Applications GSTARIMA-SUR models for rainfall data in the region Malang obtained GSTARIMA models ((1)(1,12,36),(0),(1))-SUR with determination coefficient generated with the average of 57.726%.
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Weiss, Andrew A. "A Comparison of Ordinary Least Squares and Least Absolute Error Estimation." Econometric Theory 4, no. 3 (December 1988): 517–27. http://dx.doi.org/10.1017/s0266466600013438.
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In a linear-regression model with heteroscedastic errors, we consider two tests: a Hausman test comparing the ordinary least squares (OLS) and least absolute error (LAE) estimators and a test based on the signs of the errors from OLS. It turns out that these are related by the well-known equivalence between Hausman and the generalized method of moments tests. Particular cases, including homoscedasticity and asymmetry in the errors, are discussed.
Dissertations / Theses on the topic "Ordinary least square model"
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Ullah, Obaid. "The Effect of Remittances on Socio-Economic Condition and Decision Making Process of Rural Families : A Case Study of Peshawar Khyber Pukhunkhuwa Pakistan." Thesis, Södertörns högskola, Institutionen för samhällsvetenskaper, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:sh:diva-28199.
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The objective of the study is to analyze the effect of remittances on socio-economic conditions of rural families in Peshawar, and how remittances change the decision-making process of families. This study examines the effects of remittances on local population in three towns of Peshawar, i.e. Hayatabad, Faqir-Abad and Gulberg-area. The study is basically conducted to see how remittances bring changes in the life of the recipients. The research is quantitative, data was collected through questionnaires. For the research, a sample of 300 respondents was selected from three towns in Peshawar District. The results of the research were interpreted through statistical tools, regression model. The Ordinary least square method (OLS) was used to find out the effect of remittances on health sector and investment made by respondents in the form of purchased property and investment made in establishing their own businesses. Along with that, the simple Logit Model was also used. The result is generalized on linear model with link logit and binomial response. Findings stated that the literacy level of the emigrant’s household positively impacted on children’s probability to move to private education. The results reveals that the recipient invest 4% of their income on investment as a form of business and purchasing property while educated people appears not to be interested in investing money in businesses as they want to continue their jobs. Respondent are not using a large portion of their remittances for health perhaps mostly people are entitled to free services from the government. Remittances changed their standard of living and socio economic affairs of the remittances receiving respondents. According to my demographic information about respondents life, the remittances mostly brings changes in the physical part of people life such as Private Education, Personal transport, new houses, investment in buying properties, more electric appliances, bank balance etc., but culturally people are still conservative and believe in cultural values that have been practices by local population for centuries. Following are some main recommendations that this research has identified, Foreign remittances are improving the socio-economic conditions and decision making of rural families, however, their consumption behavior is not inclined towards investment, they rather prefer to enjoy luxurious household’s equipment. Therefore, it is recommended that these families should utilize their resources in more efficient way, and should go for short and long term investment avenues. Government must design policies in order to educate these families and create awareness among them that will enable them to look for sustainable domestic income, which would help the families in the long run. These families follow the norms and traditions, and they prefer sending their children abroad for earning purposes, which in turn is comparatively less profitable idea as compared to sending these children for higher education purpose.
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Gaspard, Guetchine. "FLOOD LOSS ESTIMATE MODEL: RECASTING FLOOD DISASTER ASSESSMENT AND MITIGATION FOR HAITI, THE CASE OF GONAIVES." OpenSIUC, 2013. https://opensiuc.lib.siu.edu/theses/1236.
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This study aims at developing a model to estimate flood damage cost caused in Gonaives, Haiti by Hurricane Jeanne in 2004. In order to reach this goal, the influence of income, inundation duration and inundation depth, slope, population density and distance to major roads on the loss costs was investigated. Surveyed data were analyzed using Excel and ArcGIS 10 software. The ordinary least square and the geographically weighted regression analyses were used to predict flood damage costs. Then, the estimates were delineated using voronoi geostatistical map tool. As a result, the factors account for the costs as high as 83%. The flood damage cost in a household varies between 24,315 through 37,693 Haitian Gourdes (approximately 607.875 through 942.325 U.S. Dollars). Severe damages were spotted in the urban area and in the rural section of Bassin whereas very low and low losses are essentially found in Labranle. The urban area was more severely affected by comparison with the rural area. Damages in the urban area are estimated at 41,206,869.57USD against 698,222,174.10 17,455,554.35USD in the rural area. In the urban part, damages were more severe in Raboteau-Jubilée and in Downtown but Bigot-Parc Vincent had the highest overall damage cost estimated at 9,729,368.95 USD. The lowest cost 7,602,040.42USD was recorded in Raboteau. Approximately, 39.38% of the rural area underwent very low to moderate damages. Bassin was the most severely struck by the 2004 floods, but Bayonnais turned out to have the highest loss cost: 4,988,487.66 USD. Bassin along with Labranle had the least damage cost, 2,956,131.11 and 2,268,321.41 USD respectively. Based on the findings, we recommended the implementation and diversification of income-generating activities, the maintenance and improvement of drains, sewers and gullies cleaning and the establishment of conservation practices upstream of the watersheds. In addition, the model should be applied and validated using actual official records as reference data. Finally, the use of a calculation-based approach is suggested to determine flood damage costs in order to reduce subjectivity during surveys.
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Shulga, Yelena A. "Model-based calibration of a non-invasive blood glucose monitor." Digital WPI, 2006. https://digitalcommons.wpi.edu/etd-theses/58.
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This project was dedicated to the problem of improving a non-invasive blood glucose monitor being developed by the VivaScan Corporation. The company has made some progress in the non-invasive blood glucose device development and approached WPI for a statistical assistance in the improvement of their model in order to predict the glucose level more accurately. The main goal of this project was to improve the ability of the non-invasive blood glucose monitor to predict the glucose values more precisely. The goal was achieved by finding and implementing the best regression model. The methods included ordinary least squared regression, partial least squares regression, robust regression method, weighted least squares regression, local regression, and ridge regression. VivaScan calibration data for seven patients were analyzed in this project. For each of these patients, the individual regression models were built and compared based on the two factors that evaluate the model prediction ability. It was determined that partial least squares and ridge regressions are two best methods among the others that were considered in this work. Using these two methods gave better glucose prediction. The additional problem of data reduction to minimize the data collection time was also considered in this work.
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Hu, Yajie. "Exploring Equity and Resilience of Transportation Network through Modeling Travel Behavior: A Study of OKI Region." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1554212469614412.
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Alothman, Ahmad. "Model-Free Variable Selection For Two Groups of Variables." Diss., Temple University Libraries, 2018. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/515925.
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StatisticsPh.D.
In this dissertation we introduce two variable selection procedures for multivariate responses. Our procedures are based on sufficient dimension reduction concepts and are model-free. In the first procedure we consider the dual marginal coordinate hypotheses, where the role of the predictor and the response is not important. Motivated by canonical correlation analysis (CCA), we propose a CCA-based test for the dual marginal coordinate hypotheses, and devise a joint backward selection algorithm for dual model-free variable selection. The second procedure is based on ordinary least squares (OLS). We derive and study the asymptotic properties of the OLS-based test under the normality assumption of the predictors as well as an asymmetry assumption. When these assumptions are violated, the asymptotic test with elliptical trimming and clustering is still valid with desirable numerical performances. A backward selection algorithm for the predictor is also provided for the OLS-based test. The performances of the proposed tests and the variable selection procedures are evaluated through synthetic examples and a real data analysis.
Temple University--Theses
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Murff, Elizabeth J. Tipton. "On the efficiency of ranked set sampling relative to simple random sampling for estimating the ordinary least squares parameters of the simple linear regression model /." Full text (PDF) from UMI/Dissertation Abstracts International, 2001. http://wwwlib.umi.com/cr/utexas/fullcit?p3008403.
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Cankaya, Ergin Cagatay. "Testing methods for calibrating Forest Vegetation Simulator (FVS) diameter growth predictions." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/97321.
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The Forest Vegetation Simulator (FVS) is a growth and yield modeling system widely-used for predicting stand and tree-level attributes for management and planning applications in North American forests. The accuracy of FVS predictions for a range of tree and stand level attributes depends a great deal on the performance of the diameter increment model and its predictions of change in diameter at breast height (DBH) over time. To address the challenge of predicting growth in highly variable and geographically expansive forest systems, FVS was designed to include an internal calibration algorithm that makes use of growth observations, when available, from permanent inventory plots. The basic idea is that observed growth rates on a collection of remeasured trees are used to adjust or "calibrate" FVS diameter growth predictions. Therefore, DBH modeling was the focus of this investigation.
Five methods were proposed for local calibration of individual tree DBH growth predictions and compared to two sets of results generated without calibration. Data from the US Forest Service's Forest Inventory and Analysis (FIA) program were used to test the methods for eleven widely-distributed forest tree species in Virginia. Two calibration approaches were based on median prediction errors from locally-observed DBH increments spanning a five year average time interval. Two were based on simple linear regression models fitted to the locally-observed prediction errors, and one method employed a mixed effects regression model with a random intercept term estimated from locally-observed DBH increments. Data witholding, specifically a leave-one-out cross-validation was used to compare results of the methods tested.
Results showed that any of the calibration approaches tested in general led to improved accuracy of DBH growth predictions, with either of the median-based methods or regression based methods performing better than the random-effects-based approach. Equivalence testing showed that median or regression-based local calibration methods met error tolerances within ± 12% of observed DBH increments for all species with the random effects approach meeting a larger tolerance of ± 17%. These results showed improvement over uncalibrated models, which failed to meet tolerances as high as ± 30% for some species in a newly-fitted DBH growth model for Virginia, and as high as ± 170% for an existing model fitted to data from a much larger region of the Southeastern United States. Local calibration of regional DBH increment models provides an effective means of substantially reducing prediction errors when a relatively small set of observations are available from local sources such as permanent forest inventory plots, or the FIA database.MS
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Choudhury, Gias Uddin Ahmed. "Impact of Microcredit Program on Women's Empowerment in Rural Bangladesh." Thesis, Karlstads universitet, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-80192.
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Background – This study is an attempt to explore the relationship between microcredit and the socio-economic empowerment of women in rural Bangladesh. Microcredit is simply the extension of a small amount of collateral-free institutional loans to jointly liable poor group members to generate employment and income enhancing activities. As it is too difficult for poor members to get loan from the formal credit institutions, Grameen Bank (GB) or other Non-Government Organizations (NGOs) provide small loans to vulnerable groups of the society by which they are expected to empower over his counterparts. Research questions – RQ1: How does micro-credit affect different indicators of women empowerment in the rural areas of Bangladesh? RQ2– Is the impact different from the male counterparts in the sample households? Purpose – This study is an effort to find the impact of microcredit on a number of indicators of women’s empowerment in the rural areas in Bangladesh. Methodology – Quantitative Regression Techniques such as Ordinary Least Square (OLS) and Instrumental Variable (IV) method have been applied to get the relationship between microcredit and women empowerment. Conclusion – Applying nationally representative cross-section survey data, Bangladesh Integrated Household Survey (BIHS) 2015, this thesis is intended to find the causal linkage between microcredit and women empowerment’s with different dimensions of women’s decisions are taken as empowerment indicators: production, resources, income, leadership, savings and time. The analysis has been conducted at the household level. The study assumes that women empowerment is endogenous. After controlling for endogeneity in the estimation by using an instrumental variable (IV) ‘distance to the market’ this study finds a significant relationship between microcredit and different dimensions of women’s empowerment. Participation in the microcredit program is found to be significant in explaining some of the outcome indicators of empowerment for the sampled households.
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Chu, Ka Lok 1975. "Inequalities and equalities associated with ordinary least squares and generalized least squares in partitioned linear models." Thesis, McGill University, 2004. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=85140.
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The motivation for this thesis is the paper by Paul L. Canner [The American Statistician, vol. 23, no. 5, pp. 39--40 (1969)] in which it was noted that in simple linear regression it is possible for the generalized least squares regression line to lie either entirely above or entirely below all of the observed data points.Chapter I builds on the observation that in Canner's model the ordinary least squares and generalized least squares regression lines are parallel, which led us to introduce a new measure of efficiency of ordinary least squares and to find conditions for which the total Watson efficiency of ordinary least squares in a partitioned linear model exceeds or is less than the product of the two subset Watson efficiencies, i.e., the product of the Watson efficiencies associated with the two subsets of parameters in the underlying partitioned linear model.
We introduce the notions of generalized efficiency function, efficiency factorization multiplier, and determinantal covariance ratio, and obtain several inequalities and equalities. We give special attention to those partitioned linear models for which the total Watson efficiency of ordinary least squares equals the product of the two subset Watson efficiencies. A key characterization involves the equality between the squares of a certain partial correlation coefficient and its associated ordinary correlation coefficient.
In Chapters II and IV we suppose that the underlying partitioned linear model is weakly singular in that the column space of the model matrix is contained in the column space of the covariance matrix of the errors in the linear model. In Chapter III our results are specialized to partitioned linear models where the partitioning is orthogonal and the covariance matrix of the errors is positive definite.
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Studený, Marek. "Modelování tržní ceny nemovitosti mnohonásobnou lineární regresí." Master's thesis, Vysoké učení technické v Brně. Ústav soudního inženýrství, 2013. http://www.nusl.cz/ntk/nusl-232776.
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The main subject of the diploma thesis is a market price modeling by real estates. As a tool for modeling, is used a multiple linear regression. As starting points, are used an econometrical theory and knowledge about real estate valuation. The main goal is to find optimal model for best capture in the time and place.
Books on the topic "Ordinary least square model"
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Gribaudo, Michael Louis. Development of a system model and least mean square (LMS) filter for the Naval Postgraduate School (NPS) Infrared Search and Target Designation (IRSTD) system. Monterey, Calif: Naval Postgraduate School, 1989.
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Witkov, Carey, and Keith Zengel. Chi-Squared Data Analysis and Model Testing for Beginners. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198847144.001.0001.
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This book is the first to make chi-squared model testing, one of the data analysis methods used to discover the Higgs boson and gravitational waves, accessible to undergraduate students in introductory physics laboratory courses. By including uncertainties in the curve fitting, chi-squared data analysis improves on the centuries old ordinary least squares and linear regression methods and combines best fit parameter estimation and model testing in one method. A toolkit of essential statistical and experimental concepts is developed from the ground up with novel features to interest even those familiar with the material. The presentation of one- and two-parameter chi-squared model testing, requiring only elementary probability and algebra, is followed by case studies that apply the methods to simple introductory physics lab experiments. More challenging topics, requiring calculus, are addressed in an advanced topics chapter. This self-contained and student-friendly introduction to chi-squared analysis and model testing includes a glossary, end-of-chapter problems with complete solutions, and software scripts written in several popular programming languages, that the reader can use for chi-squared model testing. In addition to introductory physics lab students, this accessible introduction to chi-squared analysis and model testing will be of interest to all who need to learn chi-squared model testing, e.g. beginning researchers in astrophysics and particle physics, beginners in data science, and lab students in other experimental sciences.
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Walsh, Bruce, and Michael Lynch. Analysis of Short-term Selection Experiments: 1. Least-squares Approaches. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198830870.003.0018.
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This chapter examines short-term (a few generations) selection response in the mean of a trait. Traditionally, such experiments are analyzed using least-squares (LS) approaches. While ordinary LS (OLS) is often used, genetic drift causes the residual to be both correlated and heteroscedastic, resulting in the sampling variances given by OLS being too small. This chapter details the appropriate general LS (GLS) approaches to properly account for this residual error structure. It also reviews some of the common features observed in short-term selection experiments and examines experimental designs, such as the use of a control population versus a divergence-selection approach. It concludes by discussing another linear model used mainly by plant breeders, generation-means analysis (GMA), wherein remnant seed for several generations of response are crossed and then grown in a common garden. Such an analysis can provide insight into the genetic nature of any response.
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Kritz, Mary M., and Douglas T. Gurak. International Student Mobility. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198815273.003.0011.
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This chapter examines the role that sending country structural factors play in influencing the proportion of tertiary students studying abroad. It examines how outbound mobility ratio (OMR) responds to sending county supply and demand for tertiary education, population size, per capital GDP, development, education expenditures, and other factors. In all Ordinary Least Squares (OLS) and fixed-effect model specifications, the OMR had a negative relationship to tertiary supply. While countries with larger populations send more students abroad, they have smaller OMRs. Fixed-effects models also showed that changes in tertiary supply and the percentage of GDP spent on tertiary education were negatively related to OMRs. The chapter reviews government scholarship programmes sponsored by Global South countries and the practices they pursue to encourage student return and strengthen tertiary capacity in science, technology, engineering, and mathematics (STEM). These programmes in developing countries in Africa, Asia, and Latin America are changing international student flows.
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Brazier, John, Julie Ratcliffe, Joshua A. Salomon, and Aki Tsuchiya. Modelling health state valuation data. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780198725923.003.0005.
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This chapter examines the technical issues in modelling health state valuation data. Most measures of health define too many states to directly value all of them (e.g. SF-6D defines 18,000 health states). The solution has been to value a subset and by using modelling to predict the values of all states. This chapter reviews two approaches to modelling: one using multiattribute utility theory to determine health values given an assumed functional form; and the other is using statistical modelling of SF-6D preference data that are skewed, bimodal, and clustered by respondents. This chapter examines the selection of health states for valuation, data preparation, model specification, and techniques for modelling the data starting with ordinary least squares (OLS) and moving on to more complex techniques including Bayesian non-parametric and semi-parametric approaches, and a hybrid approach that combines cardinal preference data with the results of paired data from a discrete choice experiment.
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Li, Quan. Using R for Data Analysis in Social Sciences. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190656218.001.0001.
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This book seeks to teach undergraduate and graduate students in social sciences how to use R to manage, visualize, and analyze data in order to answer substantive questions and replicate published findings. This book distinguishes itself from other introductory R or statistics books in three ways. First, targeting an audience rarely exposed to statistical programming, it adopts a minimalist approach and covers only the most important functions and skills in R that one will need for conducting reproducible research projects. Second, it emphasizes meeting the practical needs of students using R in research projects. Specifically, it teaches students how to import, inspect, and manage data; understand the logic of statistical inference; visualize data and findings via histograms, boxplots, scatterplots, and diagnostic plots; and analyze data using one-sample t-test, difference-of-means test, covariance, correlation, ordinary least squares (OLS) regression, and model assumption diagnostics. Third, it teaches students how to replicate the findings in published journal articles and diagnose model assumption violations. The principle behind this book is to teach students to learn as little R as possible but to do as much reproducible, substance-driven data analysis at the beginner or intermediate level as possible. The minimalist approach dramatically reduces the learning cost but still proves adequate information for meeting the practical research needs of senior undergraduate and beginning graduate students. Having completed this book, students can use R and statistical analysis to answer questions regarding some substantively interesting continuous outcome variable in a cross-sectional design.
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Halperin, Sandra, and Oliver Heath. 16. Patterns of Association. Oxford University Press, 2017. http://dx.doi.org/10.1093/hepl/9780198702740.003.0016.
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This chapter discusses the principles of bivariate analysis as a tool for helping researchers get to know their data and identify patterns of association between two variables. Bivariate analysis offers a way of establishing whether or not there is a relationship between two variables, a dependent variable and an independent variable. With bivariate analysis, theoretical expectations can be compared against evidence from the real world to see if the theory is supported by what is observed. The chapter examines the pattern of association between dependent and independent variables, with particular emphasis on hypothesis testing and significance tests. It discusses ordinary least squares (OLS) regression and cross-tabulation, two of the most widely used statistical analysis techniques in political research. Finally, it explains how to state the null hypothesis, calculate the chi square, and establishing the correlation between the dependent and independent variables.
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Bezuidenhout, Anne. Contextualism and Semantic Minimalism. Edited by Yan Huang. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199697960.013.31.
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The debate between contextualists and semantic minimalists about meaning/content is one that matters most to philosophers of language, even though the debate is not solely a philosophical one. There are at least three ways of casting the debate. Firstly, it can be cast as one about how and when semantic and pragmatic mental resources are used during ordinary conversational exchanges. This debate utilizes theories and methodologies from psychology. Secondly, it can be framed in terms of the logic of natural languages and how to incorporate context sensitivity into a formal, compositional model of natural-language sentence-level meaning. Thirdly, it can be approached from an analytic philosophy of language perspective, with the aim of clarifying various crucial concepts, such as the concepts of saying and implicating, using a priori methods. Ideally, these domains of research will produce outcomes that cohere with each other. This essay surveys recent progress in these three domains.
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Rajeev, S. G. Spectral Methods. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198805021.003.0013.
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Thenumerical solution of ordinary differential equations (ODEs)with boundary conditions is studied here. Functions are approximated by polynomials in a Chebychev basis. Sections then cover spectral discretization, sampling, interpolation, differentiation, integration, and the basic ODE. Following Trefethen et al., differential operators are approximated as rectangular matrices. Boundary conditions add additional rows that turn them into square matrices. These can then be diagonalized using standard linear algebra methods. After studying various simple model problems, this method is applied to the Orr–Sommerfeld equation, deriving results originally due to Orszag. The difficulties of pushing spectral methods to higher dimensions are outlined.
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Ross, Andrew. Bird on Fire. Oxford University Press, 2011. http://dx.doi.org/10.1093/oso/9780199828265.001.0001.
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Phoenix, Arizona is one of America's fastest growing metropolitan regions. It is also its least sustainable one, sprawling over a thousand square miles, with a population of four and a half million, minimal rainfall, scorching heat, and an insatiable appetite for unrestrained growth and unrestricted property rights. In Bird on Fire, eminent social and cultural analyst Andrew Ross focuses on the prospects for sustainability in Phoenix--a city in the bull's eye of global warming--and also the obstacles that stand in the way. Most authors writing on sustainable cities look at places like Portland, Seattle, and New York that have excellent public transit systems and relatively high density. But Ross contends that if we can't change the game in fast-growing, low-density cities like Phoenix, the whole movement has a major problem. Drawing on interviews with 200 influential residents--from state legislators, urban planners, developers, and green business advocates to civil rights champions, energy lobbyists, solar entrepreneurs, and community activists--Ross argues that if Phoenix is ever to become sustainable, it will occur more through political and social change than through technological fixes. Ross explains how Arizona's increasingly xenophobic immigration laws, science-denying legislature, and growth-at-all-costs business ethic have perpetuated social injustice and environmental degradation. But he also highlights the positive changes happening in Phoenix, in particular the Gila River Indian Community's successful struggle to win back its water rights, potentially shifting resources away from new housing developments to producing healthy local food for the people of the Phoenix Basin. Ross argues that this victory may serve as a new model for how green democracy can work, redressing the claims of those who have been aggrieved in a way that creates long-term benefits for all. Bird on Fire offers a compelling take on one of the pressing issues of our time--finding pathways to sustainability at a time when governments are dismally failing their responsibility to address climate change.
Book chapters on the topic "Ordinary least square model"
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Zdaniuk, Bozena. "Ordinary Least-Squares (OLS) Model." In Encyclopedia of Quality of Life and Well-Being Research, 4515–17. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-007-0753-5_2008.
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Jin, Ying. "Spatial Economics, Urban Informatics, and Transport Accessibility." In Urban Informatics, 115–32. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8983-6_8.
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AbstractOne central pillar in the development of urban science which is key to the development of simulation of models of urban structure is spatial econometrics. In this chapter, we outline the way in which ideas pertaining to accessibility which we define conventionally, as in transport economics, as the relative nearness and size of locations to one another, can be embedded in a wider econometric framework. We are thus able to explore how GDP (gross domestic product) of different locations is influenced by different spatial investments. To illustrate this, we first outline the intellectual context, followed by a review of the most relevant econometric models. We examine the data required for such models and look at various quantifications in terms of elasticities of business productivity with respect to transport accessibility, using ordinary least squares, time-series fixed effects, and a range of dynamic panel-data models which narrow down the valid range of estimates. We then show how the model is applied to Guangdong province (with its connections to Hong Kong and Macau), which is one of the three major mega-city regions and a leading adopter of new technologies in China.
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Butterweck, Hans J. "Traveling-Wave Model of Long LMS Filters." In Least-Mean-Square Adaptive Filters, 35–78. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2005. http://dx.doi.org/10.1002/0471461288.ch2.
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Larbi, Isaac, Clement Nyamekye, Fabien C. C. Hountondji, Gloria C. Okafor, and Peter Rock Ebo Odoom. "Climate Change Impact on Climate Extremes and Adaptation Strategies in the Vea Catchment, Ghana." In African Handbook of Climate Change Adaptation, 1–17. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-42091-8_95-1.
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AbstractClimate change impact on rainfall and temperature extreme indices in the Vea catchment was analyzed using observation and an ensemble mean of bias-corrected regional climate models datasets for Representative Concentration Pathway (RCP 4.5) scenario. Rainfall extreme indices such as annual total wet-day precipitation (PRCPTOT), extremely wet days (R99P), consecutive wet days (CWD), consecutive dry days (CDD), and temperature indices such as warmest day (TXx) and warmest night (TNx) from the Expert Team on Climate Change Detection Monitoring Indices (ETCCDMI) were computed for both the historical (1986–2016) and future (2020–2049) period using the RClimdex. The parametric ordinary least square (OLS) regression approach was used to detect trends in the time series of climate change and extreme indices. The results show an increase in mean annual temperature at the rate of 0.02 °C/year and a variability in rainfall at the catchment, under RCP 4.5 scenario. The warmest day and warmest night were projected to increase by 0.8 °C and 0.3 °C, respectively, in the future relative to the historical period. The intensity (e.g., R99p) and frequency (e.g., CDD) of extreme rainfall indices were projected to increase by 29 mm and 26 days, respectively, in the future. This is an indication of the vulnerability of the catchment to the risk of climate disasters (e.g., floods and drought). Adaptation strategies such as early warning systems, availability of climate information, and flood control measures are recommended to reduce the vulnerability of the people to the risk of the projected impact of climate extreme in the future.
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Larbi, Isaac, Clement Nyamekye, Fabien C. C. Hountondji, Gloria C. Okafor, and Peter Rock Ebo Odoom. "Climate Change Impact on Climate Extremes and Adaptation Strategies in the Vea Catchment, Ghana." In African Handbook of Climate Change Adaptation, 1937–53. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-45106-6_95.
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AbstractClimate change impact on rainfall and temperature extreme indices in the Vea catchment was analyzed using observation and an ensemble mean of bias-corrected regional climate models datasets for Representative Concentration Pathway (RCP 4.5) scenario. Rainfall extreme indices such as annual total wet-day precipitation (PRCPTOT), extremely wet days (R99P), consecutive wet days (CWD), consecutive dry days (CDD), and temperature indices such as warmest day (TXx) and warmest night (TNx) from the Expert Team on Climate Change Detection Monitoring Indices (ETCCDMI) were computed for both the historical (1986–2016) and future (2020–2049) period using the RClimdex. The parametric ordinary least square (OLS) regression approach was used to detect trends in the time series of climate change and extreme indices. The results show an increase in mean annual temperature at the rate of 0.02 °C/year and a variability in rainfall at the catchment, under RCP 4.5 scenario. The warmest day and warmest night were projected to increase by 0.8 °C and 0.3 °C, respectively, in the future relative to the historical period. The intensity (e.g., R99p) and frequency (e.g., CDD) of extreme rainfall indices were projected to increase by 29 mm and 26 days, respectively, in the future. This is an indication of the vulnerability of the catchment to the risk of climate disasters (e.g., floods and drought). Adaptation strategies such as early warning systems, availability of climate information, and flood control measures are recommended to reduce the vulnerability of the people to the risk of the projected impact of climate extreme in the future.
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Danlami, Abubakar Hamid, and Shri Dewi Applanaidu. "Sustaining a Cleaner Environment by Curbing Down Biomass Energy Consumption." In African Handbook of Climate Change Adaptation, 1–17. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-42091-8_211-1.
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AbstractEnvironmental degradation, soil erosion, and desertification are some of the consequences of high rate of traditional biomass fuel use by households in developing countries. The critical issues to raise here are how can these households be encouraged to change their energy consumption behavior? What are the factors that cause the rampant use of biomass fuel in developing countries? How and to what extent can these factors be manipulated so that households in developing countries are encouraged to adopt clean energy fuel an alternative to the most widely used biomass fuel? Therefore, this chapter tries to find answer to the above questions raised, by carrying out an in depth analysis of households’ use of biomass fuel in developing countries using Bauchi State, Nigeria, as the case study. Cluster area sampling technique was utilized to generate the various responses, where a total number of 539 respondents were analyzed. The study estimated ordered logit model to analyze the factors that influence the movement of households along the energy ladder from nonclean energy to the cleaner energy. Furthermore, Ordinary Least Squares (OLS) model was estimated to analyze the impacts of socio-economic, residential, and environmental factors on biomass energy consumption. It was found that age of the household head and his level of education, income, living in urban areas, home ownership, and hours of electricity supply have positive and significant impact on household energy switching from traditional biomass energy use to the cleaner energy. Therefore, policies that will enhance household income and the increase in the availability of cheap cleaner energy will encourage households switching to cleaner energy sources thereby reducing the level of environmental pollution in the study area.
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Danlami, Abubakar Hamid, and Shri Dewi Applanaidu. "Sustaining a Cleaner Environment by Curbing Down Biomass Energy Consumption." In African Handbook of Climate Change Adaptation, 1423–39. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-45106-6_211.
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AbstractEnvironmental degradation, soil erosion, and desertification are some of the consequences of high rate of traditional biomass fuel use by households in developing countries. The critical issues to raise here are how can these households be encouraged to change their energy consumption behavior? What are the factors that cause the rampant use of biomass fuel in developing countries? How and to what extent can these factors be manipulated so that households in developing countries are encouraged to adopt clean energy fuel an alternative to the most widely used biomass fuel? Therefore, this chapter tries to find answer to the above questions raised, by carrying out an in depth analysis of households’ use of biomass fuel in developing countries using Bauchi State, Nigeria, as the case study. Cluster area sampling technique was utilized to generate the various responses, where a total number of 539 respondents were analyzed. The study estimated ordered logit model to analyze the factors that influence the movement of households along the energy ladder from nonclean energy to the cleaner energy. Furthermore, Ordinary Least Squares (OLS) model was estimated to analyze the impacts of socio-economic, residential, and environmental factors on biomass energy consumption. It was found that age of the household head and his level of education, income, living in urban areas, home ownership, and hours of electricity supply have positive and significant impact on household energy switching from traditional biomass energy use to the cleaner energy. Therefore, policies that will enhance household income and the increase in the availability of cheap cleaner energy will encourage households switching to cleaner energy sources thereby reducing the level of environmental pollution in the study area.
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Song, Chao, and Jinming Hao. "Scalar Weighed Least Square Combination Model for Clock Offset Prediction." In Lecture Notes in Electrical Engineering, 539–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29175-3_49.
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Nong, Xiuli. "A New Fuzzy Linear Regression Model for Least Square Estimate." In Information and Business Intelligence, 709–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29087-9_109.
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Molina, Francisco J., and Pierre Pegon. "Least-Square Effective Stiffness to be Used for Equivalent Linear Model." In Lecture Notes in Civil Engineering, 133–49. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73932-4_10.
Full textConference papers on the topic "Ordinary least square model"
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Harun, Suriyati, Yasmin Yahya, Nurashikin Saaludin, and Wan Suriyani Che Wan Ahmad. "Comparison of ordinary least square and mixed-effect regression models for estimation of tree diameter increment." In IMCOM '15: The 9th International Conference on Ubiquitous Information Management and Communication. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2701126.2701167.
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Liu, Jingdong, Won-Ho Choi, and Fei Hao. "Research on the Influencing Factors of Film Box Office Based on Ordinary Least Square and Threshold Quantile Autoregressive Model." In 2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS). IEEE, 2019. http://dx.doi.org/10.1109/iucc/dsci/smartcns.2019.00065.
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Gro¨nstedt, Tomas. "Least Squares Based Transient Nonlinear Gas Path Analysis." In ASME Turbo Expo 2005: Power for Land, Sea, and Air. ASMEDC, 2005. http://dx.doi.org/10.1115/gt2005-68717.
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A method for computing least squares estimates for transient nonlinear gas path analysis is derived. The solution to the optimal estimation problem is found by solving a system of nonlinear equations. A single iteration of the equation system requires integrating an extended set of nonlinear ordinary differential algebraic equations (ODAE). The additional differential equations originate from the differentiation of the least squares expression used to define optimality. The numerical efficiency of the extended ODAE algorithm is assessed by comparing it to an optimization based method. To illustrate the derived estimation technique a complete model of the Frank Whittle W1 engine is given within the paper. An example of the implementation of the extended ODAE method is demonstrated in the framework of this model. The performance of the method is also discussed and evaluated on a full nonlinear transient model of the RM12 fighter engine.
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Silva, Clodoaldo J., and Mohammed F. Daqaq. "On Utilizing Approximate Modal Shapes to Estimate the Effective Nonlinearity of a Structural Mode." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-71113.
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This effort investigates the accuracy of estimating the effective nonlinearity of a given vibration mode using approximate modal shapes. As an example, the problem of approximating the modal effective nonlinearities of a linearly-tapered cantilever beam (along the width) is considered. This example was intentionally selected because the linear eigenvalue problem cannot be solved analytically for the exact eigenfrequencies and actual linear mode shapes of the structure, which permits investigating the influence of approximating the mode shapes on the effective nonlinearity. The nonlinear partial differential equation governing the beam’s motion is first discretized into an infinite set of nonlinear ordinary differential equations. The method of multiple scales is then utilized to obtain an approximate analytical expression for the effective nonlinearity which depends on the assumed mode shapes used in the series discretization. To approximate the mode shapes, three methods were utilized: i) a crude approach which directly utilizes the linear modes of a regular (untapered) cantilever beam to estimate the effective nonlinearity, ii) a finite element approach wherein the structural modes are obtained in ANSYS, then fitted into orthonormal polynomial curves while minimizing the least square error in calculating the eigenfrequencies, and iii) a Rayleigh-Ritz approach which utilizes a set of orthonormal trial basis functions to construct the structural mode shapes as a linear combination of the trial functions used. A comparison among the three methods for eight different taperings reveals that, while the modal frequencies are well-approximated yielding less than 2% deviation among the three methods, there is a huge discrepancy in approximating the nonlinear coefficients including the effective nonlinearity. This leads to the conclusion that convergence of the eigenfrequencies is not sufficient for accurate estimation of the nonlinear parameters.
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Özer, Ali, Aslı Cansın Doker, and Adem Türkmen. "Analysis of Capital Flight in Developing Countries: A Study on Turkey between 1980 and 2010." In International Conference on Eurasian Economies. Eurasian Economists Association, 2013. http://dx.doi.org/10.36880/c04.00702.
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The aim of this study is to determine whether there is a relationship between Capital flight and some macroeconomic variables by using anual data between 1980 and 2010 in Turkey. Capital flight measured by World Bank (1985) method, was used as dependent variable and external debt, foreign direct investment, uncertainty, real GDP growth, exchange rates, trade balance and consumer price index were used as independent variables. Ordinary Least squares estimation method, Johansen-Jeselius cointegration test, Granger causality test and variance decomposition results produced by VEC model were used in the study. After those econometrics and economics analysis, this paper put forward that there is a long run relationship between some macroeconomic variables and capital flight.The results show external debt, foreign direct investment inflows, and foreign reserves to be the major effector of capital flight.
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Noveski, Martin, Nina Mojsova Kjoseva, and Sasho Kjosev. "GOVERNMENT INDEBTEDNESS AND ECONOMIC GROWTH IN THE REPUBLIC OF NORTH MACEDONIA." In Economic and Business Trends Shaping the Future. Ss Cyril and Methodius University, Faculty of Economics-Skopje, 2020. http://dx.doi.org/10.47063/ebtsf.2020.0001.
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Government consumption plays an important role for stability of the national economy, especially in periods of economic crisis. However, rapidly growing public debt is a concerning issue nowadays, since it might jeopardize economic growth perspectives. Economic theory suggests that public debt has non-linear impact on economic growth in a form of inverted U-shape. In other words, it is believed that after a certain threshold, public debt will have negative impact on economic growth. Given that such threshold varies significantly across countries, the aim of this paper is to calculate the turning point of the public debt impact in the Republic of North Macedonia. For this purpose, we use non-linear multiple regression model for real GDP growth rate as dependent variable, general government public debt-to-GDP ratio (in nominal and squared terms) as key independent variable, as well as several other controlling variables. Since theory also suggests reverse causality between economic growth and public debt, we use three different estimation techniques (Ordinary Least Squares, Two-Stages Least Squares, and Generalized Method of Moments) to deal with potential endogeneity, as well as to cross-validate the results. Our results show that general government debt in the Republic of North Macedonia positively affects economic growth until it reaches around 30% of GDP, whereas further indebtedness after that turning point will most likely have negative impact. Given that current debt level is slightly above 40% (10 percentage points higher than the turning point), whereby due to the COVID-19 crisis it is expected to grow even more in the upcoming years, the need of urgent fiscal consolidation inevitably arises. In this regards, deeper and more comprehensive analysis is needed in order to identify adequate channels for its efficient and effective implementation.
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Klutse, Senanu Kwasi. "The problem of economic growth in Sub-Saharan Africa." In The Challenges of Analyzing Social and Economic Processes in the 21st Century. Szeged: Szegedi Tudományegyetem Gazdaságtudományi Kar, 2020. http://dx.doi.org/10.14232/casep21c.9.
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A wide range of policy-related variables have a persistent influence on economic growth. This has consistently maintained the interest of economists on the determinants of economic growth over the years. There is consensus however that for countries to grow sustainably, a lot of stall must be placed on higher savings rate as this makes it easy for such countries to grow faster because they endogenously allocate more resources to inventive activities. Due to data difficulties in Sub-Saharan Africa (SSA) it is nearly impossible for one to consider important variables such as accumulation of knowledge and human capital when analysing growth sustainability. Studying four lower middle-income countries in SSA – Ghana, Republic of Congo, Kenya and Lesotho – this study tests the hypothesis of sustainable growth by using a Dynamic Ordinary Least Square (DOLS) model to examine the relationship between savings, investment, budget deficit and the growth variable. The results showed that savings had a significant but negative relationship with the GDP per capita (PPP). A Granger Causality test conducted showed that savings does not granger cause GDP per capita (PPP), the HDI index, deficit and investment. This leads to the conclusion that growth in these countries are not sustainable. The study recommends that policy makers focus on the savings variable if these countries will want to achieve sustainable growth.
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TIWARI, R., and V. K. SRIVASTAVA. "LARGE SAMPLE ASYMPTOTIC PROPERTIES OF ORDINARY LEAST SQUARES, TWO STAGE LEAST SQUARES AND LIMITED INFORMATION MAXIMUM LIKELIHOOD ESTIMATORS IN SIMULTANEOUS EQUATIONS MODELS." In Proceedings of Statistics 2001 Canada: The 4th Conference in Applied Statistics. PUBLISHED BY IMPERIAL COLLEGE PRESS AND DISTRIBUTED BY WORLD SCIENTIFIC PUBLISHING CO., 2002. http://dx.doi.org/10.1142/9781860949531_0027.
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Balekelayi, Ngandu, and Solomon Tesfamariam. "Time Dependent Reliability Analysis for Oil and Gas Pipelines: A Bayesian Spectral Analysis-Based Deterioration Model." In 2020 13th International Pipeline Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/ipc2020-9284.
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Abstract Oil and gas pipelines are essential infrastructures that sustain the economy of modern society. They are designed for continuous and reliable operations over their service lives. Once installed, however, their reliability is affected by several threats among which external corrosion plays a significant role. Corrosion-based pit depth growth reduces the wall thickness over time that consequently affect the mechanical strength and the hydraulic performance of the pipeline. Pipeline utility managers rely on the corrosion growth rate models to plan their maintenance, rehabilitation and/or replacement. Existing pipeline deterioration models are mostly based on the power law function that relates the pit depth with the exposure time and rarely include the soil factors that can have effect on the corrosion growth rate. Moreover, the way these factors affect the corrosion rate is complex and cannot be captured with simple linear relationship. This paper uses data found in the literature to build a nonlinear pit depth growth model based on Bayesian spectral analysis regression technique. All continuous covariates are allowed to have smooth nonlinear spectral representations of their effect function on the pit depth growth. The discrete (i.e. categorical) factors are modeled using the ordinary least squared algorithm. The final semiparametric model allows to capture all pit depth measurements, even those difficult to be modeled using high degree polynomials. The stochastic nature of the pit depth growth is captured through the Bayesian approach. A time dependent reliability analysis using subset simulation is carried out to evaluate the changes occurring in the probability of failure of the pipe over time and allow for a better planning and management of these important infrastructure. The model is applied on a bare pipe directly exposed to the soil environment over time. The Bayesian pit depth growth model is accurate enough to allow the computation of the time dependent reliability of pipelines considering both the mechanical and hydraulic reliabilities.
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Blazheska, Angela, and Igor Ivanovski. "QUANTITATIVE ANALYSIS OF THE OPERATIONAL PERFORMANCE OF THE SELECTED NON-LIFE INSURANCE COMPANIES IN THE INSURANCE MARKET OF REPUBLIC OF NORTH MACEDONIA." In Economic and Business Trends Shaping the Future. Ss Cyril and Methodius University, Faculty of Economics-Skopje, 2020. http://dx.doi.org/10.47063/ebtsf.2020.0030.
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The aim of this paper is to analyze the operational performance of the 5 dominant companies on the non-life insurance market in Republic of North Macedonia. As input in the analysis, the quarterly data for the 2009-2019 period is included for the key indicators such as the gross written premium (GWP), the gross liquidated damages, the number of insurance contracts and settled claims as well as the operating costs of the companies. These variables are observed through OLS (Ordinary Least Squares) regression analysis and VAR (Vector Autoregressive) model which demonstrates the dependence of the GWP to the rest of the indicators and their responsiveness to shocks. The findings of the study offers valuable insight and opportunities for short term recommendations and further exploration. The companies are missing the sustainability and viability of their management models and define the “shortcism” as more important for the market and operational performance. In these regard, the business models must introduce contemporary and comprehensive tools and techniques, dominantly based on IT solutions and adequate HCM changes, for risk identification and actions for lowering the claims ratio and their volume. Moreover, all the companies should evaluate the elements of the operating costs, both for sales as well as of the administrative ones, as critical components for the companies’ profitability. Very importantly, significant changes at the ALM models and higher rate of returns should inevitably create additional advantage for dynamic and sustainable models for consumer acquisition and new products and services development.
Reports on the topic "Ordinary least square model"
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CAPACITY EVALUATION OF EIGHT BOLT EXTENDED ENDPLATE MOMENT CONNECTIONS SUBJECTED TO COLUMN REMOVAL SCENARIO. The Hong Kong Institute of Steel Construction, September 2021. http://dx.doi.org/10.18057/ijasc.2021.17.3.6.
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The extended stiffened endplate (8ES) connection is broadly used in the seismic load-resisting parts of steel structures. This connection is prequalified based on the AISC 358 standard, especially for seismic regions. To study this connection’s behaviors, in the event of accidental loss of a column, the finite element model results were verified against the available experimental data. A parametric study using the finite element method was then carried out to investigate these numerical models’ maximum capacity and effective parameters' effect on their maximum capacity in a column loss scenario. This parametric analysis demonstrated that these connections fail at the large displacement due to the catenary action mode at the rib stiffener's vicinity. The carrying capacity, PEEQ, Von-Mises stress, middle column force-displacement, critical bolt axial load, and the beam axial load curves were discussed. Finally, using the Least Square Method (LSM), a formula is presented to determine the displacement at the maximum capacity of these connections. This formula can be used in this study's presented method to determine the maximum load capacity of the 8ES connections in a column loss scenario.