Relevant bibliographies by topics / Multiply regression

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Academic literature on the topic 'Multiply regression'

Author: Grafiati
Published: 5 June 2025
Last updated: 24 June 2025

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Journal articles on the topic "Multiply regression"

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Chaurasia, Ashok, and Ofer Harel. "Using AIC in multiple linear regression framework with multiply imputed data." Health Services and Outcomes Research Methodology 12, no. 2-3 (2012): 219–33. http://dx.doi.org/10.1007/s10742-012-0088-8.

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Klein, Martin D., John Zylstra, and Bimal K. Sinha. "Finite Sample Inference for Multiply Imputed Synthetic Data under a Multiple Linear Regression Model." Calcutta Statistical Association Bulletin 71, no. 2 (2019): 63–82. http://dx.doi.org/10.1177/0008068318803814.

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In this article, we develop finite sample inference based on multiply imputed synthetic data generated under the multiple linear regression model. We consider two methods of generating the synthetic data, namely posterior predictive sampling and plug-in sampling. Simulation results are presented to confirm that the proposed methodology performs as the theory predicts and to numerically compare the proposed methodology with the current state-of-the-art procedures for analysing multiply imputed partially synthetic data. AMS 2000 subject classification: 62F10, 62F25, 62J05
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Golubova, T. N., N. M. Ovsyannikova, Z. R. Makhkamova, and I. Yu Tkachenko. "Multiply Regression Analysis as An Integrated Approach in the TB Indicators Assessment in the Republic of Crimea." Medicina 9, no. 2 (2021): 100–112. http://dx.doi.org/10.29234/2308-9113-2021-9-2-100-112.

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The use of multiply regression analysis for the TB indicators assessment and prediction in the Republic of Crimea is discussed. Basic TB indicators in the Republic of Crimea for the period 2014-2018 were studied using Pearson correlation coefficient and step-by-step multiply linear regression analysis. During paired correlation analysis, dependable association with an average and high degree is identified for the most indicators of interest. The multiple correlation coefficients were higher than the paired correlation coefficients, which demonstrated the greater significance of the group conditionality of the indicators. For key epidemiological TB indicators the regression equations are constructed and the most relevant predictors are found using stepwise multiply regression analysis. The regression equations for TB incidence, prevalence and mortality rates are proposed for practical applying to predict epidemiological situation.
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Li, Wei, Yuwen Gu, and Lan Liu. "Demystifying a class of multiply robust estimators." Biometrika 107, no. 4 (2020): 919–33. http://dx.doi.org/10.1093/biomet/asaa026.

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Summary For estimating the population mean of a response variable subject to ignorable missingness, a new class of methods, called multiply robust procedures, has been proposed. The advantage of multiply robust procedures over the traditional doubly robust methods is that they permit the use of multiple candidate models for both the propensity score and the outcome regression, and they are consistent if any one of the multiple models is correctly specified, a property termed multiple robustness. This paper shows that, somewhat surprisingly, multiply robust estimators are special cases of doubly robust estimators, where the final propensity score and outcome regression models are certain combinations of the candidate models. To further improve model specifications in the doubly robust estimators, we adapt a model mixing procedure as an alternative method for combining multiple candidate models. We show that multiple robustness and asymptotic normality can also be achieved by our mixing-based doubly robust estimator. Moreover, our estimator and its theoretical properties are not confined to parametric models. Numerical examples demonstrate that the proposed estimator is comparable to and can even outperform existing multiply robust estimators.
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Wang, Xiaorui, Guoyou Qin, Xinyuan Song, and Yanlin Tang. "Censored quantile regression based on multiply robust propensity scores." Statistical Methods in Medical Research 31, no. 3 (2021): 475–87. http://dx.doi.org/10.1177/09622802211060520.

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Censored quantile regression has elicited extensive research interest in recent years. One class of methods is based on an informative subset of a sample, selected via the propensity score. Propensity score can either be estimated using parametric methods, which poses the risk of misspecification or obtained using nonparametric approaches, which suffer from “curse of dimensionality.” In this study, we propose a new estimation method based on multiply robust propensity score for censored quantile regression. This method only requires one of the multiple candidate models for propensity score to be correctly specified, and thus, it provides a certain level of resistance to the misspecification of parametric models. Large sample properties, such as the consistency and asymptotic normality of the proposed estimator, are thoroughly investigated. Extensive simulation studies are conducted to assess the performance of the proposed estimator. The proposed method is also applied to a study on human immunodeficiency viruses.
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Sun, Yilun, Lu Wang, and Peisong Han. "Multiply robust estimation in nonparametric regression with missing data." Journal of Nonparametric Statistics 32, no. 1 (2019): 73–92. http://dx.doi.org/10.1080/10485252.2019.1700254.

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Han, Peisong. "Multiply Robust Estimation in Regression Analysis With Missing Data." Journal of the American Statistical Association 109, no. 507 (2014): 1159–73. http://dx.doi.org/10.1080/01621459.2014.880058.

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Lu, Wenqi, Guoyou Qin, Zhongyi Zhu, and Dongsheng Tu. "Multiply robust subgroup identification for longitudinal data with dropouts via median regression." Journal of Multivariate Analysis 181 (January 2021): 104691. http://dx.doi.org/10.1016/j.jmva.2020.104691.

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von Hippel, Paul T. "4. Regression with Missing Ys: An Improved Strategy for Analyzing Multiply Imputed Data." Sociological Methodology 37, no. 1 (2007): 83–117. http://dx.doi.org/10.1111/j.1467-9531.2007.00180.x.

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When fitting a generalized linear model—such as linear regression, logistic regression, or hierarchical linear modeling—analysts often wonder how to handle missing values of the dependent variable Y. If missing values have been filled in using multiple imputation, the usual advice is to use the imputed Y values in analysis. We show, however, that using imputed Ys can add needless noise to the estimates. Better estimates can usually be obtained using a modified strategy that we call multiple imputation, then deletion (MID). Under MID, all cases are used for imputation but, following imputation, cases with imputed Y values are excluded from the analysis. When there is something wrong with the imputed Y values, MID protects the estimates from the problematic imputations. And when the imputed Y values are acceptable, MID usually offers somewhat more efficient estimates than an ordinary MI strategy.
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Asyrofa, Rahmi, and Firdaus Mahmudy Wayan. "Regression Modelling for Precipitation Prediction Using Genetic Algorithms." TELKOMNIKA Telecommunication, Computing, Electronics and Control 15, no. 3 (2017): 1290–300. https://doi.org/10.12928/TELKOMNIKA.v15i3.4028.

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This paper discusses the formation of an appropriate regression model in precipitation prediction. Precipitation prediction has a major influence to multiply the agricultural production of potatoes in Tengger, East Java, Indonesia. Periodically, the precipitation has non-linear patterns. By using a non-linear approach, the prediction of precipitation produces more accurate results. Genetic algorithm (GA) functioning chooses precipitation period which forms the best model. To prevent early convergence, testing the best combination value of crossover rate and mutation rate is done. To test the accuracy of the predicted results are used Root Mean Square Error (RMSE) as a benchmark. Based on the RMSE value of each method on every location, prediction using GA-Non-Linear Regression is better than Fuzzy Tsukamoto for each location. Compared to Generalized Space-Time Autoregressive-Seemingly Unrelated Regression (GSTAR-SUR), precipitation prediction using GA is better. This has been proved that for 3 locations GA is superior and on 1 location, GA has the least value of deviation level.
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Dissertations / Theses on the topic "Multiply regression"

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Peraça, Maria da Graça Teixeira. "Modelos para estimativa do grau de saturação do concreto mediante variáveis ambientais que influenciam na sua variação." reponame:Repositório Institucional da FURG, 2009. http://repositorio.furg.br/handle/1/3436.

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Dissertação(mestrado) - Universidade Federal do Rio Grande, Programa de Pós-Graduação em Engenharia Oceânica, Escola de Engenharia, 2009.<br>Submitted by Lilian M. Silva (lilianmadeirasilva@hotmail.com) on 2013-04-22T19:51:54Z No. of bitstreams: 1 Modelos para estimativa do Grau de Saturação do concreto mediante Variáveis Ambientais que influenciam na sua variação.pdf: 2786682 bytes, checksum: df174dab02a19756db94fc47c6bb021d (MD5)<br>Approved for entry into archive by Bruna Vieira(bruninha_vieira@ibest.com.br) on 2013-06-03T19:20:55Z (GMT) No. of bitstreams: 1 Modelos para estimativa do Grau de Saturação do concreto mediante Variáveis Ambientais que influenciam na sua variação.pdf: 2786682 bytes, checksum: df174dab02a19756db94fc47c6bb021d (MD5)<br>Made available in DSpace on 2013-06-03T19:20:55Z (GMT). No. of bitstreams: 1 Modelos para estimativa do Grau de Saturação do concreto mediante Variáveis Ambientais que influenciam na sua variação.pdf: 2786682 bytes, checksum: df174dab02a19756db94fc47c6bb021d (MD5) Previous issue date: 2009<br>Nas engenharias, é fundamental estimar o tempo de vida útil das estruturas construídas, o que neste trabalho significa o tempo que os íons cloretos levam para atingirem a armadura do concreto. Um dos coeficientes que influenciam na vida útil do concreto é o de difusão, sendo este diretamente influenciado pelo grau de saturação (GS) do concreto. Recentes estudos levaram ao desenvolvimento de um método de medição do GS. Embora esse método seja eficiente, ainda assim há um grande desperdício de tempo e dinheiro em utilizá-lo. O objetivo deste trabalho é reduzir estes custos calculando uma boa aproximação para o valor do GS com modelos matemáticos que estimem o seu valor através de variáveis ambientais que influenciam na sua variação. As variáveis analisadas nesta pesquisa, são: pressão atmosférica,temperatura do ar seco, temperatura máxima, temperatura mínima, taxa de evaporação interna (Pichê), taxa de precipitação, umidade relativa, insolação, visibilidade, nebulosidade e taxa de evaporação externa. Todas foram analisadas e comparadas estatisticamente com medidas do GS obtidas durante quatro anos de medições semanais, para diferentes famílias de concreto. Com essas análises, pode-se medir a relação entre estes dados verificando que os fatores mais influentes no GS são, temperatura máxima e umidade relativa. Após a verificação desse resultado, foram elaborados modelos estatísticos, para que, através dos dados ambientais, cedidos pelo banco de dados meteorológicos, se possam calcular, sem desperdício de tempo e dinheiro, as médias aproximadas do GS para cada estação sazonal da região sul do Brasil, garantindo assim uma melhor estimativa do tempo de vida útil em estruturas de concreto.<br>In engineering, it is fundamental to estimate the life-cycle of built structures, which in this study means the period of time required for chlorides to reach the concrete reinforcement. One of the coefficients that affect the life-cycle of concrete is the diffusion, which is directly influenced by the saturation degree (SD) of concrete. Recent studies have led to the development of a measurement method for the SD. Although this method is efficient, there is still waste of time and money when it is used. The objective of this study is to reduce costs by calculating a good approximation for the SD value with mathematical models that predict its value through environmental variables that affect its variation. The variables analysed in the study are: atmospheric pressure, temperature of the dry air, maximum temperature, minimum temperature, internal evaporation rate (Pichê), precipitation rate, relative humidity, insolation, visibility, cloudiness and external evaporation rate. All of them were statistically analysed and compared with measurements of SD obtained during four years of weekly assessments for different families of concrete. By considering these analyses, the relationship among these data can be measured and it can be verified that the most influent variables affecting the SD are the maximum temperature and the relative humidity. After verifying this result, statistical models were developed aiming to calculate, based on the environmental data provided by the meteorological database and without waste of time and money, the approximate averages of SD for each seasonal station of the south region of Brazil, thus providing a better estimative of life-cycle for concrete structures.
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Ernstsson, Hampus, and Liljesvan Max Börjes. "Multiples for Valuation Estimates of Life Science Companies in Sweden." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254239.

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Market multiples are a common and simple tool for estimation of corporate value. It can express temporal dynamics and differences in markets, industries and firms. Despite their practical usefulness, some critical problems remains which continue to be debated. This thesis investigates if there exists characteristics for explaining market capitalization by market multiples within the life science industry in Sweden. The approach follows well known theory of multiple linear regression analysis. The results indicated only a linear relationship between the market cap and the R\&amp;D expenditures of a company. This does not mean that the other explanatory variables does not have effect on market cap only that there is no linear relationship that could be statistically proven.<br>Värderingsmultiplar är ett vanligt och enkelt verktyg för att approximera företags värde. Det kan beskriva temporär dynamik och skillnader hos marknader, industrier och bolag. Trots dess praktiska användbarhet finns en del kritiska problem som fortfarande debatteras. Denna uppsats undersöker om det existerar några egenskaper för att förklara marknadsvärdet med hjälp av värderingsmultiplar inom life science industrin i Sverige. Tillvägagångssättet följer välkänd teori om multipel linjär regressions analys. Resultaten visade att det endast finns ett samband mellan marknadsvärdet och utgifter för forskning och utveckling för ett bolag. Detta innebär inte att de andra variablerna inte har någon effekt på marknadsvärdet, utan att det inte finns ett linjärt samband som kan bevisas på ett statistiskt vis.
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Krebs-Brown, Axel Johannes. "Shrinkage and calibration in multiple regression." Thesis, University of Warwick, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.364602.

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Azmi, Muhammad Arsalan Raza. "Workers' Compensation Modeling Using Multiple Regression." Thesis, North Dakota State University, 2018. https://hdl.handle.net/10365/28850.

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Workers? compensation insurance (WCI) is the highest cost to an employer following accidents. It is needed to predict the benefits value without taking into account the past records of an employee, which is not readily available in most cases. Employment and workers? compensation data were acquired from the Bureau of Labor Statistics and the National Academy of Social Insurance, respectively. The statistical model was developed with SAS using multiple regression and the process was simplified using analysis of covariance (ANCOVA). The model predicted future values of workers compensation given a known number of covered workers for all U.S. states. The model is statistically proven to be fit for all states. The states were compared on the basis of percentage deviation from the actual values. By using this model, insurance companies and policymakers can have better understanding of workers? compensation trend and they can quotes premiums and develop policies more accurately.
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Hauser, Andrea. "Building a risk map for hurricane-force tropical cyclones in continental Portugal." Master's thesis, Instituto Superior de Economia e Gestão, 2021. http://hdl.handle.net/10400.5/23306.

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Mestrado Bolonha em Actuarial Science<br>Tropical cyclones have enormous destructive potential. In 2018 continental Por- tugal has been affected by hurricane Leslie, the weather-related event having the highest impact ever on the property portfolio of the portuguese insurance company Fidelidade, causing several millions euros of losses. The fear is that, in the near future, the occurrence of this type of events increases in intensity and frequency, as a consequence of the climate change due to the warming of the planet. Quantifying the potential loss to which the property portfolio of Fidelidade could be subject to, helps in approximately determining premiums and capital reserves, as well as in defining the coverage to be provided. In this work, an approach to model the costs caused by a tropical cyclone extreme event is presented. The model is based on the losses incurred by the property port- folio of Fidelidade due to hurricane Leslie. By using the estimated models, it is possible to produce cost estimates for different scenarios of interest for the com- pany. The estimated models are also used to build a risk map for the councils of continental Portugal. The results obtained indicate that the councils with the estimated higher average cost ratio are all located along the coast of the country.<br>Ciclones tropicais têm um enorme potencial de destruição. Em 2018, Portugal continental foi atingido pelo furacão Leslie, que constituiu o fenómeno metereológico de maior impacto, até à data, no portfolio da companhia de seguros Fidelidade, causando milhões de euros em perdas. De facto, os ciclones tropicais têm um enorme potencial de destruição. A preocupação é que, em breve, a ocorrência deste tipo de fenómenos aumente em intensidade e frequência, como consequência das mudanças climáticas provocadas pelo aquecimento global. Quantificar a potencial perda à qual a companhia Fidelidade pode estar sujeita ajuda a determinar aproximadamente os prémios e provisões, assim como a definir a cobertura a ser providenciada. Neste trabalho, é apresentada uma abordagem para modelar os custos causados por um ciclone tropical extremo. O modelo é baseado nas perdas provocadas ao portfolio da Fidelidade pelo furacão Leslie. Ao usar os modelos, é possível produzir custos estimados para diferentes cenários de interesse da companhia. Os modelos estimados sã também utilizados para construir um mapa de risco para os conselhos de Portugal continental. Os resultados obtidos indicam que os conselhos com a maior taxa média de custos estimada estão localizados ao longo da costa do país.<br>info:eu-repo/semantics/publishedVersion
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Carvalho, Tânia Maria de [UNESP]. "Modelagem digital de atributos de solo da Fazenda Edgárdia - Botucatu-SP." Universidade Estadual Paulista (UNESP), 2016. http://hdl.handle.net/11449/148704.

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Submitted by TÂNIA MARIA DE CARVALHO null (taniacarvalho2010@gmail.com) on 2017-02-02T19:26:12Z No. of bitstreams: 1 TESE_arquiv.pdf: 4743361 bytes, checksum: 0c094f892ee8b02e1690df7e4438651f (MD5)<br>Approved for entry into archive by LUIZA DE MENEZES ROMANETTO (luizamenezes@reitoria.unesp.br) on 2017-02-06T16:42:11Z (GMT) No. of bitstreams: 1 carvalho_tm_dr_bot.pdf: 4743361 bytes, checksum: 0c094f892ee8b02e1690df7e4438651f (MD5)<br>Made available in DSpace on 2017-02-06T16:42:11Z (GMT). No. of bitstreams: 1 carvalho_tm_dr_bot.pdf: 4743361 bytes, checksum: 0c094f892ee8b02e1690df7e4438651f (MD5) Previous issue date: 2016-12-19<br>O mapa de solos é uma ferramenta essencial para o planejamento de uso da terra e estudos que envolvem aspectos ambientais relativos a esse importante recurso natural. Técnicas quantitativas e ferramentas de geoprocessamento têm sido aliadas à interpretação dos processos pedogenéticos para possibilitar a elaboração de mapas mais precisos, obtidos por processo mais rápido e menos oneroso. Dentre os modelos aplicados, os denominados modelos híbridos empregam variáveis auxiliares preditoras e autocorrelação espacial, para viabilizar a predição de atributos de solo em locais não amostrados. A iniciativa para mapeamento digital do solo em escala mundial – GlobalSoilMap.net atua no sentido de disponibilizar representações globais de atributos de solo, elaboradas por meio da aplicação de modelo híbrido em dados legados de solos, realizando a prática do Mapeamento Digital de Solos (MDS). Com base nesse princípio, esse trabalho baseou-se na hipótese de que a aplicação da técnica híbrida regressão-krigagem, utilizando dados legados de levantamento de solo e covariáveis de relevo e sensoriamento remoto proveem mapa de atributos de solo representativos de uma área da Cuesta de Botucatu. O modelo foi aplicado localmente, a duas profundidades, para representação contínua do Índice de Avermelhamento (IAV), saturação de bases (V%), teor de areia, teor de argila, CTC e pH dos solos da Fazenda Experimental Edgárdia, para a qual são disponíveis dados de levantamento de solo. As covariáveis preditoras derivadas de um MDE e de imagem orbital foram uniformizadas a uma resolução espacial de 10 m, e os métodos foram selecionados de acordo com a verificação de correlação linear significativa entre atributos e covariáveis e autocorrelação espacial dos atributos ou dos resíduos de regressões lineares múltiplas (RLM). Os dados foram separados em subconjuntos de treinamento e validação. Os coeficientes de correlação entre atributos de solo e covariáveis foram significativos e variaram de -0,40 a 0,51. Os preditores mais correlacionados aos atributos foram Índice Topográfico de Umidade (ITU), Declividade (Decl), Aspecto (Aspc), Elevação (Elev) e índice de vegetação NDVI, sendo os quatro últimos os principais na estimação das frações texturais. Os valores de R² ajustado das RLM, entre 0,10 e 0,36, foram considerados baixos. De modo geral, os mapas de predição expuseram padrões característicos da variação espacial observada nos mapas das covariáveis preditoras, usadas na calibração dos modelos. Foi observado um incremento na acurácia entre as duas etapas do processo de RK, indicando que o mapa final é superior em relação à RLM. No entanto, os modelos apresentaram, de modo geral, um baixo desempenho quando avaliados por meio de validação externa, mesmo com a estratificação em duas áreas mais uniformes em termos de relevo. Os resultados indicaram a limitação do uso de amostragem para fins de levantamento em modelos de predição. Houve ainda dificuldade de aplicação dos modelos em função do contexto litológico complexo e da dinâmica local de formação de solos, que não puderam ser detectadas pelas covariáveis selecionadas. Apesar das limitações, os mapas de predição apresentaram coerência com o conhecimento relativo aos atributos, nas condições locais.<br>The soil map is an essential tool for land use planning and studies related to environmental aspects of this important natural resource. Quantitative techniques and geoprocessing tools are currently combined with the interpretation of pedogenic processes to enable the development of more accurate maps obtained by faster and less costly process. Among the models applied to it, the hybrid models employ predictive auxiliary variables and spatial autocorrelation, to enable the prediction of soil attributes in unsampled locations. The digital soil mapping worldwide project – GlobalSoilMap.net acts in order to provide global representations of soil attributes developed through the application of hybrid model in legacy soil data, performing the practice of Digital Soil Mapping (MDS). This work was based on the assumption that the application of the hybrid technique of regression-kriging (RK), using legacy data of soil survey and covariates of relief and remote sensing provide representative map of soil attributes of an area in Cuesta of Botucatu. The goal was to apply locally, in two depths, prediction models and continuous representation of Soil Redness Index (IAV), base saturation index (V%), sand content and clay content, cation-exchange capacity (CTC) and pH of the soils in Edgardia Experimental Farm, for which are available soil survey data. The predictor covariates were derived from an Digital Elevation Model (MDE) and an orbital image. They were all standardized at spatial resolution of 10 m, the methods were selected by checking significant linear correlation between attributes and covariates and spatial autocorrelation of attributes or residues of multiple linear regressions (RLM). The data were separated into training and validation subsets. The correlation coefficients (r) between soil attributes and covariates were significant and ranged from -0.40 to 0.51. The predictors more correlated to attributes were topographic wetness index (ITU), slope (Decl), aspect (Aspc), elevation (Elev) and vegetation index (NDVI), and the last four are key definers of granulometric fractions. The values of adjusted R² of RLM were between 0.10 and 0.36, which is considered low. In general, the prediction maps exhibited characteristic patterns of spatial variation observed in the covariates maps, used in the calibration of the models. An increase in accuracy was observed between the two steps of the modeling process by RK, indicating that the final map is better than the RLM. However, the models showed generally low performance, and did not provide good results when evaluated by external validation and even if the area was stratified in two smaller plots, with more homogeneous relief. The results indicated the restricted use of soil survey sampling in prediction models, and the difficulty of applying MDS in areas with complex lithology, especially where the correlation between local dynamics of soil genesis and selected covariates are not strong. Despite the limitations, the prediction maps were consistent with knowledge about soil properties in local conditions.
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Nordin, Henrik, and Gustav Klockby. "Bestämningsfaktorer till regionala bostadspriser : En analys av de svenska länen för perioden 1993-2012." Thesis, Linköpings universitet, Nationalekonomi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-111160.

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Bostadsmarknaden är en av de största tillgångsmarknaderna i ett land varpå förändringar i bostadspriserna får långt gångna konsekvenser för det enskilda hushållet, det finansiella systemet och samhällsekonomin i stort. Flertalet tidigare studier har analyserat den svenska bostadsmarknaden utifrån ett storstadsperspektiv alternativt jämfört Sveriges bostadsmarknad mot andra länder. Vi har identifierat att studiet kring vad som bestämmer prisnivån på regionala bostadsmarknader i Sverige är tämligen oexploaterat varför avsikten med den här studien är att analysera bestämningsfaktorer till de svenska bostadspriserna på länsnivå. Sålunda är ett bidragande mål med denna studie att tillföra en bättre förståelse för dynamiken på den svenska bostadsmarknaden. I studien använder vi multipel regression där vi bearbetar paneldata med en Fixed Effect Model. Ett flertal förtester har gjorts för att få fram den mest tillförlitliga modellen i vilken vi skattat bostadspriserna utifrån teoretiskt belagda förklaringsvariabler. De slutsatser vi har dragit är att disponibel inkomst, befolkningstäthet och sysselsättningsgrad kan förklara bostadspriserna på länsnivå med en procents signifikansnivå. Skillnaden i bostadspriserna mellan länen har relativt sett ökat över tidsperioden för studien. Avslutningsvis diskuteras uppvisade avvikelser mellan de verkliga bostadspriserna och de skattade bostadspriserna vilka kan förklaras av att bostadsmarknaden är känslostyrd med inslag av spekulationer.<br>The housing market is one of the greatest assets markets in a given country. Therefore, changes in housing prices have a big impact on the single household, the financial system and the economic system as a whole. Due to the housing markets vital role in the society, many scientific studies have been done with the purpose of enlighten and discover the dynamics of the Swedish housing market. The focuses in these earlier studies have more than often taken a metropolitan perspective or compared the Swedish housing market with other countries. However, this study divides the Swedish housing market into regional county level with the purpose of analyzing determinants of housing prices due to county specific variables. By analyzing the housing prices due to county specific factors a contributing goal with this study is to deepen the understanding about the dynamics in the Swedish housing market. In this study we have used multiple regressions in order to work with panel data. The Fixed Effect Model fitted our purpose well which is why that kind of model was used in order to estimate the housing prices for every single Swedish county. The conclusions drawn in this study are that disposable income, people density and employment rate are all statistically significant on one percent level in order to explain the housing price at state level. We have also discovered that, during the observed period, the relative differences in housing prices between the different states have increased. Finally, the differences found between the real housing prices and the estimated housing prices, can be explained by the assumption that the housing market is driven by emotions and speculations.
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Forslund, Gustaf, and David Åkesson. "Predicting share price by using Multiple Linear Regression." Thesis, KTH, Farkost och flyg, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-140645.

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The aim of the project was to design a multiple linear regression model and use it to predict the share’s closing price for 44 companies listed on the OMX Stockholm stock exchange’s Large Cap list. The model is intended to be used as a day trading guideline i.e. today’s information is used to predict tomorrow’s closing price. The regression was done in Microsoft Excel 2010[18] by using its built-in function LINEST. The LINEST-function uses the dependent variable y and all the covariates x to calculate the β-value belonging to each covariate. Several multiple linear regression models were created and their functionality was tested, but only seven models were better than chance i.e. more than 50 % in the right direction. To determine the most suitable model out of the remaining seven, Akaike’s Information Criterion (AIC), was applied. The covariates used in the final model were; Dow Jones closing price, Shanghai opening price, conjuncture, oil price, share’s opening price, share’s highest price, share’s lowest price, lending rate, reports, positive/negative insider trading, payday, positive/negative price target, number of completed transactions during one day, OMX Stockholm closing price, TCW index, increasing closing price three days in a row and decreasing closing price three days in a row. The maximum average deviation between the predicted closing price and the real closing price of all the 44 shares predicted were 6,60 %. In predicting the correct direction (increase or decrease) of the 44 shares an average of 61,72 % were achieved during the time period 2012-02-22 to 2013-02-20. If investing 50.000 SEK in each company i.e. a total investment of 2.2 million SEK, the total yield when using the regression model during the year 2012-02-22 to 2013-02-20 would have been 259.639 SEK (11,80 %) compared to 184.171 SEK (8,37 %) if the shares were never to be traded with during the same period of time. Of the 44 companies analysed, 31 (70,45 %) of them were profitable when using the regression model during the year compared to 30 (68,18 %) if the shares were never to be sold during the same period of time. The difference in yield in percentage between the model and keeping the shares for the year was 40,98 %.
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Saleem, Aban, and Jacob Blomgren. "Modelling Pupils’ Grades with Multiple Linear Regression Model." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-275672.

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This thesis was based on the subjects of mathematical statistics and industrial economics and management in order to analyze the grades of pupils in the final year of elementary school. The purpose was to find out what variables had a statistically significant impact on pupils’ final grades so that municipalities and schools could better understand what variables are important when trying to improve the average school results. A multiple regression model was used on data, obtained from the database of Skolverket, in order to examine what variables were statistically important. The final regression model acquired through a model reduction procedure showed that mostly structural covariates such as the academic background of pupils, percentage of female pupils and the percentage with Swedish background had a statistically significant impact on the academic performances of the students. R2 adjusted of the final model was 0.5289. The multiple regression model was discussed by referencing to previous research. In addition, the strategic management performance framework known as Balanced Scorecard which was introduced by Robert S. Kaplan and David P. Norton was used to discuss relevant key performance indicators to achieve the strategic objectives of schools.<br>Detta examensarbete, inom ämnet för matematisk statistik och industriell ekonomi, genomfördes med syftet att analysera avgångsbetygen för år 9 i den svenska skolan. Syftet var att förstå vilka variabler som hade en statistisk signifikant påverkan på elevers avgångsbetyg, så kommuner kan förstå vilka variabler som är viktiga för att förbättra de genomsnittliga skolresultaten. En regressionsanalys utfördes, på data från Skolverket, för att se vilka variabler som var statistiskt signifikanta. Den slutgiltiga regressionsmodellen, erhållen genom iterativ reducering av variabler, visade att främst strukturella kovariat, som akademisk bakgrund hos elever, andel kvinnliga studenter och andel studenter med svensk bakgrund hade en signifikant betydelse på studenters akademiska resultat. Justerad R2 var 0.5289 för den slutgiltiga modellen. I diskussionen utvärderades modellen utifrån tidigare forskning. Vidare användes teorin om balanserat styrkort, utvecklat av Robert S. Kaplan och David P. Norton, för att diskutera relevanta nyckeltal för att uppnå strategiska mål för skolan.
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Mhitarean, Ecaterina. "Marketing Mix Modelling from the multiple regression perspective." Thesis, KTH, Matematisk statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-208474.

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The optimal allocation of the marketing budget has become a difficult issue that each company is facing. With the appearance of new marketing techniques, such as online advertising and social media advertising, the complexity of data has increased, making this problem even more challenging. Statistical tools for explanatory and predictive modelling have commonly been used to tackle the problem of budget allocation. Marketing Mix Modelling involves the use of a range of statistical methods which are suitable for modelling the variable of interest (in this thesis it is sales) in terms of advertising strategies and external variables, with the aim to construct an optimal combination of marketing strategies that would maximize the profit. The purpose of this thesis is to investigate a number of regression-based model building strategies, with the focus on advanced regularization methods of linear regression, with the analysis of advantages and disadvantages of each method. Several crucial problems that modern marketing mix modelling is facing are discussed in the thesis. These include the choice of the most appropriate functional form that describes the relationship between the set of explanatory variables and the response, modelling the dynamical structure of marketing environment by choosing the optimal decays for each marketing advertising strategy, evaluating the seasonality effects and collinearity of marketing instruments. To efficiently tackle two common challenges when dealing with marketing data, which are multicollinearity and selection of informative variables, regularization methods are exploited. In particular, the performance accuracy of ridge regression, the lasso, the naive elastic net and elastic net is compared using cross-validation approach for the selection of tuning parameters. Specific practical recommendations for modelling and analyzing Nepa marketing data are provided.<br>Att fördela marknadsföringsbudgeten optimalt är en svår uppgift som alla företag ställs inför. Med uppkomsten av nya marknadsföringstekniker, som reklam på nätet och sociala media, har komplexiteten av data ökat, vilket gör detta problem ännu mer utmanande. Statistiska verktyg för förklarande och prediktiv modellering har vanligtvis använts för att hantera problemet med budgetallokering. Marknadsföringsmix Modellering är en term som omfattar klassen av statistiska metoder som är lämpliga för modellering av den intressanta variabeln (i denna uppsats är det försäljning) när det gäller reklamstrategier och externa variaber, med målet att maximera vinsten genom att konstruera en optimal kombination av marknadsstrategier. Syftet med denna uppsats är att konstruera ett antal modellbyggnadsstrategier, som även inkluderar avancerade regulariseringsmetoder för linjär regression, med en analys av fördelar och nackdelar för varje metod. Flera stora problem som den moderna marknadsföringsmix modellering står inför har beaktats, som till exempel: att välja en passande funktionsformel som bäst beskriver relationen mellan den oberoende variabeln och de beroende variablerna, att hantera marknadsföringens dynamiska omgivningar genom att välja det optimala förfallet hos varje marknadsföringsstrategi, utvärdera säsongsmässiga effekten och marknadsföringsverktygens kollinjäritet. För att överkomma de två vanligaste problemen inom marknadsföringsekonometri, som är multikollinearitet och val av variabler, har regulariseringsmetoder använts. I synnerhet har prestationsnoggrannheten av ridge regression, lasso, naive elastic net och elastic net jämförts- för att ge specifika rekommendationer för Nepa data. Parametrarna för de regulariserade regressionsmetoderna har valts genom korsvalidering. Modellens resultat visar en hög nivå av förutsägelse noggrannhet. Skillnaden mellan nämnda metoder är inte signifikanta för det givna datasetet.
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Books on the topic "Multiply regression"

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Keith, Timothy Z. Multiple Regression and Beyond. Routledge, 2019. http://dx.doi.org/10.4324/9781315162348.

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Berry, William, and Stanley Feldman. Multiple Regression in Practice. SAGE Publications, Inc., 1985. http://dx.doi.org/10.4135/9781412985208.

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Stanley, Feldman, ed. Multiple regression in practice. Sage Publications, 1985.

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Fox, John. Multiple and Generalized Nonparametric Regression. SAGE Publications, Inc., 2000. http://dx.doi.org/10.4135/9781412985154.

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Jaccard, James, and Robert Turrisi. Interaction Effects in Multiple Regression. SAGE Publications, Inc., 2003. http://dx.doi.org/10.4135/9781412984522.

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Jaccard, James. Interaction effects in multiple regression. 2nd ed. Sage Publications, 2003.

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Robert, Turrisi, and Wan Choi K, eds. Interaction effects in multiple regression. Sage Publications, 1990.

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Krebs-Brown, Axel Johannes. Shrinkage and calibration in multiple regression. typescript, 2000.

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G, West Stephen, and Reno Raymond R, eds. Multiple regression: Testing and interpreting interactions. Sage Publications, 1991.

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Orme, John G. Multiple regression with discrete dependent variables. Oxford University Press, 2009.

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Book chapters on the topic "Multiply regression"

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Rose, Valerie S., Alan P. Hill, Richard M. Hyde, and Anne Hersey. "pKa prediction in multiply-substituted phenols: A comparison of multiple linear regression and back-propagation." In Trends in QSAR and Molecular Modelling 92. Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1472-1_133.

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Olive, David J. "Multiple Linear Regression." In Linear Regression. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55252-1_2.

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Kahane, Leo H. "The Multiple Regression Model." In Regression Basics, 3rd ed. Routledge, 2024. http://dx.doi.org/10.4324/9781003349174-5.

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Dormann, Carsten. "Multiple Regression: Regression with Multiple Predictors." In Environmental Data Analysis. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55020-2_15.

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Herkenhoff, Linda, and John Fogli. "Multiple Regression." In Applied Statistics for Business and Management using Microsoft Excel. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8423-3_13.

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Canela, Miguel Ángel, Inés Alegre, and Alberto Ibarra. "Multiple Regression." In Quantitative Methods for Management. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17554-2_4.

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Takezawa, Kunio. "Multiple Regression." In Learning Regression Analysis by Simulation. Springer Japan, 2013. http://dx.doi.org/10.1007/978-4-431-54321-3_4.

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Foster, Dean P., Robert A. Stine, and Richard P. Waterman. "Multiple regression." In Business Analysis Using Regression. Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4612-0683-5_4.

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Bowers, David. "Multiple Regression." In Statistics for Economics and Business. Palgrave Macmillan UK, 1991. http://dx.doi.org/10.1007/978-1-349-21346-7_15.

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Dobson, Annette J. "Multiple regression." In An Introduction to Generalized Linear Models. Springer US, 1990. http://dx.doi.org/10.1007/978-1-4899-7252-1_6.

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Conference papers on the topic "Multiply regression"

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Yanchun, Jin, and Guan Bing. "Probing the Interaction Effect in Moderated Multiple Regression." In 2024 IEEE 7th International Conference on Electronic Information and Communication Technology (ICEICT). IEEE, 2024. http://dx.doi.org/10.1109/iceict61637.2024.10670761.

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Moura, Ricardo, Bimal Sinha, and Carlos A. Coelho. "Inference for multivariate regression model based on multiply imputed synthetic data generated via posterior predictive sampling." In APPLIED MATHEMATICS AND COMPUTER SCIENCE: Proceedings of the 1st International Conference on Applied Mathematics and Computer Science. Author(s), 2017. http://dx.doi.org/10.1063/1.4982005.

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Van Kuilenburg, Robert, and Martijn Handels. "Leveraging SME Knowledge with Big Data Multiply the Value of Both." In SPE Annual Technical Conference and Exhibition. SPE, 2022. http://dx.doi.org/10.2118/209970-ms.

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Abstract With Managed Pressure Drilling (MPD) getting more common, its rubber seals increasingly become a source of potential downtime during drilling operations. The common approach is to provide monitoring towards relevant stakeholders via expert consultants and elaborate reporting. This is costly and time-consuming. Next to monitoring and reporting, accurate prediction of the wear and tear on the rubber seal is essential. Within technology partnership, a real-time monitoring and proactive advisory tools have successfully developed and implemented. First, a scalable data infrastructure has implemented, unifying process data from the MPD and drilling systems into a single database. Second, a hybrid seal wear prediction model has been developed, combining a model-based approach with regression analyses. Key to the success was the feedback loop between operation and engineering. This included pre-and post-run inspections and measurements on the seals tied to the operational parameters. Thirdly, repetitive reports got generated automatically for daily operations, connections and seal wear. The advanced MPD condition monitoring solution was successfully developed by combining expertise in operations and engineering with data analytics skills. It emphasizes the fact that big data works best when it is part of a solid data framework, that takes domain knowledge and users into account.
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Shpagina, L. A., E. B. Logashenko, and O. S. Kotova. "CYTOKINE PROFILE IN PATIENTS WITH OCCUPATIONAL CHRONIC OBSTRUCTIVE PULMONARY DISEASE AND HEALTHY WORKERS IN CONDITIONS OF AEROSOLS CONTAINING NANOPARTICLES EXPOSURE." In The 17th «OCCUPATION and HEALTH» Russian National Congress with International Participation (OHRNC-2023). FSBSI «IRIOH», 2023. http://dx.doi.org/10.31089/978-5-6042929-1-4-2023-1-522-526.

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Diagnose of the occupational chronic obstructive pulmonary disease (COPD) at early stage is difficult because of low symptoms and altered spirography results as this COPD phenotype has substantial interstitial lung fibrosis. The aim of study was to establish the promising markers of occupational COPD due to aerosols containing nanoparticles exposure based on cytokine profile. Materials and methods. Design of the study was a pilot observational comparative. Nanoparticles assessment in the workplace air of the machine-building enterprise was done. Chemical elemental composition was measured by inductively coupled plasma atomic emission spectrometry and particles size by scanning electron microscopy. Patients with occupational chronic obstructive pulmonary disease (n=24) and healthy workers (n‑25) who worked in the studied sites and healthy people without occupational health risks (n=50) were investigated. COPD was diagnosed according to spirography criteria. Groups were matched by sex, age, smoking status. Blood serum cytokines and fibrotic markers were measured by enzyme-linked immunosorbent assay. Groups were compared by Kruskal-Wallis test, relationships were revealed by multiply linear regression. Level of significance was less than 0.01. Results. At the investigated workplaces the maximal was the mass concentration of silica nanoparticles, which was equal of 0,035 (0,29; 0,41) μg/ml. In patients with occupational COPD in comparison with healthy workers and control group the concentrations of interleukin 1‑β, C-reactive protein, monocyte chemotactic protein‑1, fibroblast growth factor‑2 (FGF‑2) — 16,1 (10,5; 18,2) pg/m amino-terminal propeptide of type-III procollagen — (PIIINP) — 92,1 (73,6; 102,4) ng/ml, matrix metalloproteinase 9, soluble vascular cellular adhesion molecule‑1 (sVCAM‑1) — 46,1 (41,5; 58,3) pg/ml. In multiple regression model nanoparticles concentration was a predictor of serum levels of PIIINP (В=0,8), FGF‑2 (В=1,9), sVCAM‑1 (В=0,7). Conclusion. PIIINP, FGF‑2, sVCAM‑1 are promising markers of occupational COPD in workers exposed to aerosols containing nanoparticles.
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Li, C. J., and H. Li. "Effect of WC-Co Addition on the Adhesion of HVOF Ni-Based Coatings." In ITSC 1998, edited by Christian Coddet. ASM International, 1998. http://dx.doi.org/10.31399/asm.cp.itsc1998p0723.

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Abstract Self-fluxing nickel based materials are widely used as hard coating materials. HVOF process is promising to produce dense coating with high adhesion. In present study the effects of HVOF spray conditions, and WC-Co addition into self-fluxing nickel based material (NiCrBSi) and the introduction of HVOF WC-Co bond coat on the adhesion of HVOF NiCrBSi coatings are investigated. With regarding to NiCrBSi material, the spray conditions are optimized by orthogonal regression experimental design method with adhesive strength. The adhesive strength is estimated by tensile test. The results show that the adhesive strength of the NiCrBSi coating sprayed at the optimized conditions reaches to about 40MPa. It is found that the addition of WC-Co material through mechanical blending can improve the adhesion of HVOF NiCrBSi coating. The adhesive strength is increased with the increase in WC-Co content in the composite powder. The introduction of HVOF WC-Co bond coat between NiCrBSi coating and substrate can improve the adhesive strength. The multiply enhancing effect on the adhesive strength of HVOF nickel based coating is recognized by applying HVOF WC-Co bond coat to NiCrBSi-WC-Co composite coating.
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Khasawneh, Mohammad Ali, and Mohammad Ahmad Alsheyab. "Modeling Thermal Conductivity, Thermal Diffusivity and Specific Heat of Asphalt Concrete Using Beta Regression and Mixture Volumetrics." In The 2nd International Conference on Civil Infrastructure and Construction. Qatar University Press, 2023. http://dx.doi.org/10.29117/cic.2023.0076.

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The main objective of this paper is to develop predictive models using Beta regression for laboratory-prepared hot mix asphalt (HMA) specimens' thermal properties, including thermal conductivity (TC), thermal diffusivity (TD) and specific heat (SH). Thirty such specimens were prepared while varying the mixture's nominal maximum aggregate sizes (NMAS) and gradation coarseness. The widely used Transient Plane Source (TPS) method was employed to determine the thermal properties of the asphalt concrete. Only one type of asphalt binder was used for preparing all specimens. The air void volume (Va) and the effective binder volume (Vbe) were calculated for each mixture. To this end, the multiple linear regressions and the non-linear beta regressions were employed. Laboratory work resulted in hundred and fifty (150) data points. Three nominal maximum aggregate sizes, two gradation coarseness levels, five replicates and five different locations of measurements to ensure accuracy and repeatability in the obtained results. In conclusion, using Va and Vbe as predictors provided reliable predictive models for the thermal properties of different asphalt mixtures. The distribution of Va and Vbe was identified, and synthetic data was created to evaluate the accuracy of the models. Apart from R2 values, beta regression was more reliable to predict thermal properties of asphalt mixtures than multiple linear regression.
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J, Bipin Nair B., S. Yadhukrishnan, and Manish A. "A Comparative Study on Document Images Classification using Logistic Regression and Multiple Linear Regressions." In 2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS). IEEE, 2023. http://dx.doi.org/10.1109/icaiss58487.2023.10250671.

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Okuno, Alex, and Alberto Ferreira. "Generalized linear tree: a flexible algorithm for predicting continuous variables." In LatinX in AI at International Conference on Machine Learning 2021. Journal of LatinX in AI Research, 2021. http://dx.doi.org/10.52591/lxai2021072420.

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Tree-based models are popular among regression methods to predict continuous variables. Also, Generalized Linear Models (GLMs) are pretty standard in many statistical applications and provide a generalization to many of the most commonly applied statistical procedures. However, in most regression tree methods, there is only one theoretical model associated for prediction in the final nodes, like multiple linear regression, logistic regressions, polynomial models, Poisson models, among others. We, therefore, propose a new tree method in which we estimate a GLM in each leaf node of the estimated tree including variable selection, new hyperparameters optimization, and tree pruning. Our method, called Generalized linear tree (GLT), has shown to be competitive compared to other well-known regression methods in real datasets, with the advantages and estimation flexibility provided by GLMs.
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Gonen, Mehmet, and Ethem Alpaydin. "Localized Multiple Kernel Regression." In 2010 20th International Conference on Pattern Recognition (ICPR). IEEE, 2010. http://dx.doi.org/10.1109/icpr.2010.352.

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Arnaldo, Ignacio, Krzysztof Krawiec, and Una-May O'Reilly. "Multiple regression genetic programming." In GECCO '14: Genetic and Evolutionary Computation Conference. ACM, 2014. http://dx.doi.org/10.1145/2576768.2598291.

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Reports on the topic "Multiply regression"

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Kubik, Harold. MLRP, Multiple Linear Regression Program. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada204565.

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Friedman, Jerome H. CLASSIFICATION AND MULTIPLE REGRESSION THROUGH PROJECTION PURSUIT. Office of Scientific and Technical Information (OSTI), 1985. http://dx.doi.org/10.2172/1447844.

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Friedman, Jerome H. Classification and Multiple Regression through Projection Pursuit. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada150797.

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Simpson, William T. Estimating air drying times of lumber with multiple regression. U.S. Department of Agriculture, Forest Service, Forest Products Laboratory, 2004. http://dx.doi.org/10.2737/fpl-rn-293.

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Cook, Steve, Peter Dawson, and Duncan Watson. Building multiple linear regression skills via ‘puzzling’ active learning. The Economics Network, 2024. http://dx.doi.org/10.53593/n4163a.

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Meloncelli, Daniel. Regression Analysis using R. Instats Inc., 2025. https://doi.org/10.61700/j3s1r521de2ee1472.

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This seminar provides a comprehensive introduction to regression analysis using R. Participants will explore the fundamentals of correlation, simple linear regression, multiple regression, and logistic regression. The seminar emphasises practical application, guiding attendees through data preparation, model fitting, assumption checking, and interpretation of results. Hands-on sessions will enable participants to apply regression techniques to real-world datasets, enhancing their analytical skills for research purposes.
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Landis, Ronald. Regression Analysis for Social and Health Science. Instats Inc., 2023. http://dx.doi.org/10.61700/me4ohse6jsdoy469.

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This comprehensive 2-day workshop will equip PhD students, professors, and professional researchers with the knowledge and skills needed to effectively use multiple regression analysis in their research. Participants will gain a deep understanding of multiple regression, learn how to use R, Excel, and Jamovi for analysis, interpret and report results, handle missing data, and enhance their research capabilities. An official Instats certificate of completion and 2 ECTS Equivalent points are provided at the conclusion of the seminar.
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Cheverud, James, Claire C. Gordon, Robert A. Walker, et al. Anthropometric Survey of US Army Personnel (1988): Correlation Coefficients and Regression Equations. Part 5. Stepwise and Standard Multiple Regression Tables. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada224990.

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Agterberg, F. P., T. J. Katsube, and S. N. Lew. Use of multiple regression for petrophysical characterization of granites as a function of alteration. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1985. http://dx.doi.org/10.4095/120273.

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Collins, Abigail. Predicting Student Success at a Large State University using Multiple Linear Regression and Hierarchical Clustering. Iowa State University, 2022. http://dx.doi.org/10.31274/cc-20240624-1143.

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