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Books on the topic 'Regression analysis model'

Author: Grafiati

Published: 5 June 2025

Last updated: 15 July 2025

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1

Draper, Norman Richard. Model selection problems. University of Toronto, Dept. of Statistics, 1986.

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2

Borowiak, Dale S. Model discrimination for nonlinear regression models. M. Dekker, 1989.

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3

Bera, Anil K. Specification test for a linear regression model with arch process. University of Illinois at Urbana-Champaign, 1993.

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4

Walter, Krämer. The linear regression model under test. Physica-Verlag, 1986.

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5

Özçam, Ahmet. The risk properties of a pre-test estimator for Zellner's seemingly unrelated regression model. Bureau of Economic and Business Research, 1990.

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6

Özçam, Ahmet. The risk properties of a pre-test estimator for Zellner's seemingly unrelated regression model. Bureau of Economic and Business Research, 1990.

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7

Chang-Jin, Kim. In search of a model that an ARCH-type model may be approximating: The Markov model of heteroskedasticity. York University, Dept. of Economics, 1990.

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8

Jiang, Jiming. Robust Mixed Model Analysis. World Scientific Publishing Co Pte Ltd, 2019.

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9

Sankoh, O. A. Influential observations in the linear regression model and Trenkler's iteration estimator. Cuvillier, 1996.

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10

Choi, ByoungSeon. ARMA model identification. Springer-Verlag, 1992.

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11

Tibshirani, Robert. The covariance inflation criterion for adaptive model selection. University of Toronto, Dept. of Statistics, 1997.

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12

Kurumai, Hiroko. MSE performance of some shrinkage estimators in a regression model with non-normal errors. Institute of Economic Research, Kobe University of Commerce, 2001.

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13

Rao, J. Sunil. Bootstrap model selection via the cost complexity parameter in regression. University of Toronto, Department of Statistics, 1993.

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14

Houston, Walter M. Empirical Bayes estimates of parameters from the logistic regression model. ACT, Inc., 1997.

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15

Houston, Walter M. Empirical Bayes estimates of parameters from the logistic regression model. ACT, Inc., 1997.

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16

LeBlanc, Michael R. Step-function covariate effects in the proportional hazards model. University of Toronto, Department of Statistics, 1993.

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17

Liman, Mohamed. Simultaneous tolerance intervals in the random one-way model with convariates. Dept. of Statistics, Oregon State University, 1985.

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18

Majumder, Agamoni. Value of statistical life: A meta-analysis with mixed effects regression model. Institute for Social and Economic Change, 2016.

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19

International, Workshop on Model-Oriented Data Analysis (6th 2001 Puchberg am Schneeberg Austria). mODa 6: Advances in model-oriented design and analysis : proceedings of the 6th International Workshop on Model-Oriented Data Analysis held in Puchberg/Schneeberg, Austria, June 25-29, 2001. Physica-Verlag, 2001.

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20

D, Lock Michael, ed. Model-free curve estimation. Chapman & Hall, 1993.

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21

Bunke, Helga, and Olaf Bunke, eds. Statistical Methods of Model Building: V2. Nonlinear Regression, Functional Relations and Robust Methods. John Wiley & Sons Ltd, 1989.

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22

G, Müller W., Wynn Henry P, Zhigli͡a︡vskiĭ A. A, and Model-Oriented Data Analysis Workshop/Conference (3rd : 1992 : Petrodvoret͡s︡, R.S.F.S.R.), eds. Model-oriented data analysis: Proceedings of the 3rd international workshop in Petrodvorets, Russia, May 25-30, 1992. Physica-Verlag, 1993.

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23

Gaver, Donald Paul. Regression analysis of hierarchical Poisson-like event rate data: Superpopulation model effect on predictions. Naval Postgraduate School, 1990.

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24

Petrucci, Alessandra. The application of a spatial regression model to the analysis and mapping of poverty. Food and Agriculture Organization of the United Nations, 2003.

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25

Moser, Barry Kurt. Linear models: A mean model approach. Academic Press, 1996.

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26

Belzil, Christian. A structural analysis of the correlated random coefficient wage regression model with an application to the ols-iv puzzle. IZA, 2005.

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27

Amiri-Simkooei, AliReza. Variance Component Estimation In Linear Models: Theoretical And Practical Aspects On Global Positioning System. VDM Verlag Dr. Müller, 2010.

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28

Almimi, Ashraf. Split-Plot Designs: Follow-Up Experiments, Missing Observations, and Model Adequacy Checking. LAP LAMBERT Academic Publishing, 2010.

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29

Tsai, Chih-Ling, and Allan D. McQuarrie. Regression and Time Series Model Selection. World Scientific Publishing Co Pte Ltd, 1998.

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30

Krämer, Walter. The Linear Regression Model Under Test. Brand: Physica, 2012.

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31

Miller, Frederic P., and Agnes F. Vandome, eds. Multivariate Adaptive Regression Splines. Alphascript Publishing, 2010.

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32

Kraemer, W., and H. Sonnberger. Linear Regression Model under Test. Physica-Verlag, 2012.

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33

Model-Free Prediction and Regression: A Transformation-Based Approach to Inference. Springer, 2015.

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34

Politis, Dimitris N. Model-Free Prediction and Regression: A Transformation-Based Approach to Inference. Springer London, Limited, 2015.

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35

An Introduction to THE LOGIT MODEL FOR ECXONOMISTS. Timberlake Consultants, 2001.

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36

Hj Jubok, Zainodin, Khuneswari Gopal Pillay, and Noraini Abdullah. Model Building Approach in Multiple Regression. UMS Press, 2018. http://dx.doi.org/10.51200/modelbuildingumspress2018-978-967-2166-14-6.

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Abstract:

This book is the outcome of our three years of research work. The problems faced by the undergraduate students in analysing the data on multiple regressions and the importance of this method had inspired the authors to come up with this book. Regression analysis is commonly used by most researchers in business, social and behavioural sciences, biological sciences and many other fields. But there is no proper procedure or approach of model-building in regression analysis. Therefore, this book is aimed at illustrating the procedures to find the best model and the model-building approach. The model-building approach is important in obtaining the best model that well describes the corresponding data set. This approach can be used in various research fields such as in economics, environmental, biological and medical sciences. The multiple regression model-building approach is applicable in various fields of research. It is very useful for researchers to obtain the best model that well describes the data. The approach is also useful in identifying the factors that will affect the dependent variable. The overall thrusts of the authors’ efforts have been geared in explaining explicitly the statistical method using regression analysis, the model building procedures and its applications, so as to meet the needs of today’s students. A substantial effort has gone in addressing multicollinearity issues and illustrating steps to overcome them. The authors hope that readers will find them helpful.

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37

Kraemer, W., and H. Sonnberger. The Linear Regression Model under Test. Physica-Verlag Heidelberg, 1999.

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38

Faraway, Julian J. Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. Taylor & Francis Group, 2013.

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39

Faraway, Julian J. Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. Taylor & Francis Group, 2004.

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40

Faraway, Julian J. Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. Taylor & Francis Group, 2005.

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41

Faraway, Julian J. Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. Taylor & Francis Group, 2005.

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42

Faraway, Julian J. Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. Taylor & Francis Group, 2005.

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43

Faraway, Julian J. Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. Taylor & Francis Group, 2005.

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44

Faraway, Julian J. Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. Taylor & Francis Group, 2013.

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45

Biswas, S. Linear Model Approach to Regression Analysis and its Applications. New Age International (P) Ltd., 1998.

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46

Davies, Patrick Laurie. Data Analysis and Approximate Models: Model Choice, Location-Scale, Analysis of Variance, Nonparametric Regression and Image Analysis. Taylor & Francis Group, 2014.

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47

Davies, Patrick Laurie. Data Analysis and Approximate Models: Model Choice, Location-Scale, Analysis of Variance, Nonparametric Regression and Image Analysis. Taylor & Francis Group, 2014.

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48

Data analysis and approximate models: Model choice, location-scale, analysis of variance, nonparametic regression and image analysis. CRC Press, 2014.

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49

Kitsos, Christos Par. Moda4-Advances in Model-Oriented Data Analysis,. Physica-Verlag, 1996.

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50

Some aspects of statistical inference of logistic regression model parameters. Acta Universitatis Upsaliensis, 1996.

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