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Books on the topic 'Classification and Regression Models'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Hesselager, Ole. Estimation of Variance Components in Hierarchical Regression Models with Nested Classification. Laboratory of Actuarial Mathematics, University of Copenhagen, 1988.

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2

Neal, Radford M. Monte Carlo implementation of Gaussian process models for Bayesian regression and classification. University of Toronto, 1997.

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3

Press, S. James. Bayesian statistics: Principles, models, and applications. Wiley, 1989.

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4

Lee, Choong Ho. A micro-scale simulation model of carbon dioxide emissions from passenger cars using classification and regression methods. National Library of Canada, 2000.

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5

Breen, Richard. Regression Models. SAGE Publications, Inc., 1996. http://dx.doi.org/10.4135/9781412985611.

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6

Ward, Michael, and Kristian Gleditsch. Spatial Regression Models. SAGE Publications, Inc., 2008. http://dx.doi.org/10.4135/9781412985888.

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7

Marsh, Lawrence, and David Cormier. Spline Regression Models. SAGE Publications, Inc., 2002. http://dx.doi.org/10.4135/9781412985901.

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8

Ward, Michael Don. Spatial regression models. Sage Publications, 2008.

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9

R, Cormier David, ed. Spline regression models. Sage Publications, 2002.

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10

Hilbe, Joseph. Logistic regression models. Chapman & Hall/CRC, 2009.

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11

Newbold, Paul, and Theodore Bos. Stochastic Parameter Regression Models. SAGE Publications, Inc., 1985. http://dx.doi.org/10.4135/9781412985994.

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12

Allison, Paul. Fixed Effects Regression Models. SAGE Publications, Inc., 2009. http://dx.doi.org/10.4135/9781412993869.

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13

William, Wasserman, and Kutner Michael H, eds. Applied linear regression models. 2nd ed. Irwin, 1989.

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14

Theodore, Bos, ed. Stochastic parameter regression models. Sage Publications, 1985.

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15

Inc, SPSS. SPSS regression models 12.0. Prentice Hall College Division, 2004.

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16

Kutner, Michael H. Applied linear regression models. 4th ed. McGraw-Hill, 2003.

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17

Theodore, Bos, ed. Stochastic parameter regression models. Sage Publications, 1985.

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18

Chris, Nachtsheim, and Neter John, eds. Applied linear regression models. 4th ed. McGraw-Hill/Irwin, 2004.

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19

Inc, SPSS, ed. SPSS regression models 9.0. SPSS Inc., 1999.

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20

Inc, SPSS, ed. SPSS regression models 12.0. SPSS Inc., 2003.

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21

John, Neter, ed. Applied linear regression models. 3rd ed. Irwin, 1996.

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22

Inc, SPSS, ed. SPSS regression models 13.0. SPSS Inc., 2004.

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23

John, Neter, ed. Applied linear regression models. 3rd ed. Irwin, 1996.

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24

Tibshirani, Robert. "Coaching" variables for regression and classification. University of Toronto, Dept. of Statistics., 1994.

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25

Hastie, Trevor. Flexible discriminant analysis: Adaptive classification. University of Toronto, Dept. of Statistics, 1992.

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26

Seber, G. A. F. Nonlinear regression. Wiley-Interscience, 2003.

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27

Godfrey, Leslie. Bootstrap Tests for Regression Models. Palgrave Macmillan UK, 2009. http://dx.doi.org/10.1057/9780230233737.

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28

Caroni, Chrysseis. First Hitting Time Regression Models. John Wiley & Sons, Inc., 2017. http://dx.doi.org/10.1002/9781119437260.

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29

Pankratz, Alan. Forecasting with Dynamic Regression Models. John Wiley & Sons, Inc., 1991. http://dx.doi.org/10.1002/9781118150528.

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30

Fahrmeir, Ludwig. Regression: Models, Methods and Applications. Springer Berlin Heidelberg, 2013.

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31

Harvey, Andrew. Seasonality in dynamic regression models. London School of Economics Centre for Economic Performance, 1994.

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32

Harvey, A. C. Seasonality in dynamic regression models. Suntory-Toyota International Centre for Economics and Related Disciplines, London School of Economics, 1993.

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33

Godfrey, L. G. Bootstrap tests for regression models. Palgrave Macmillan, 2009.

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34

Fry, John M. (John Michael), 1980-, ed. Regression: Linear models in statistics. Springer, 2010.

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35

Ferraty, Frédéric, and Philippe Vieu. A Unifying Classification for Functional Regression Modeling. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.1.

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This article presents a unifying classification for functional regression modeling, and more specifically for modeling the link between two variables X and Y, when the explanatory variable (X) is of a functional nature. It first provides a background on the proposed classification of regression models, focusing on the regression problem and defining parametric, semiparametric, and nonparametric models, and explains how semiparametric modeling can be interpreted in terms of dimension reduction. It then gives four examples of functional regression models, namely: functional linear regression mod

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36

Matloff, Norman. Statistical Regression and Classification: From Linear Models to Machine Learning. Taylor & Francis Group, 2017.

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37

Matloff, Norman. Statistical Regression and Classification: From Linear Models to Machine Learning. Taylor & Francis Group, 2017.

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38

Matloff, Norman. Statistical Regression and Classification: From Linear Models to Machine Learning. Taylor & Francis Group, 2017.

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39

Matloff, Norman. Statistical Regression and Classification: From Linear Models to Machine Learning. Taylor & Francis Group, 2017.

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40

Matloff, Norman. Statistical Regression and Classification: From Linear Models to Machine Learning. Taylor & Francis Group, 2017.

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41

Matloff, Norman. Statistical Regression and Classification: From Linear Models to Machine Learning. Taylor & Francis Group, 2017.

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42

Nguyen, Jean-Michel. ROP Model: Numerical and Nonparametric Classification-Regression. Wiley & Sons, Incorporated, John, 2018.

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43

Nguyen, Huy Hoang, and Paul N. Adams. Building Statistical Models in Python: Develop Useful Models for Regression, Classification, Time Series, and Survival Analysis. de Gruyter GmbH, Walter, 2023.

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44

Whitenack, Daniel. Machine Learning With Go: Implement Regression, Classification, Clustering, Time-series Models, Neural Networks, and More using the Go Programming Language. Packt Publishing - ebooks Account, 2017.

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45

James, Gareth. Sparseness and functional data analysis. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.11.

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This article considers two functional data analysis settings where sparsity becomes important: the first involves only measurements at a relatively sparse set of points and the second relates to variable selection in a functional case. It begins with a discussion of two data sets that fall into the ‘sparsely observed’ category, the ‘growth’ data and the ‘nephropathy’ data, both of which are used to illustrate alternative approaches for analysing sparse functional data. It then examines different classes of methods that can be applied to functional data, such as basis functions, mixed-effects m

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46

Breiman, Leo, Jerome H. Friedman, Richard A. Olshen, and Charles J. Stone. Classification And Regression Trees. Routledge, 2017. http://dx.doi.org/10.1201/9781315139470.

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47

Matloff, Norman. Statistical Regression and Classification. Chapman and Hall/CRC, 2017. http://dx.doi.org/10.1201/9781315119588.

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48

Breiman, Leo. Classification and Regression Trees. CRC Press LLC, 2017.

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49

Breiman, Leo. Classification and Regression Trees. CRC Press LLC, 2017.

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50

Loh, Wei-yin, and N. Vanichsetakul. Tree Structured Classification & Regression. John Wiley & Sons, 2001.

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