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Books on the topic 'Principal component regression and principal component analysis'

Author: Grafiati

Published: 4 June 2025

Last updated: 23 June 2025

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1

Zhang, Wenfei. Regression based principal component analysis for sparse functional data with applications to screening pubertal growth paths. [publisher not identified], 2012.

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2

Jolliffe, I. T. Principal Component Analysis. Springer New York, 1986. http://dx.doi.org/10.1007/978-1-4757-1904-8.

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3

Jolliffe, I. T. Principal component analysis. 2nd ed. Springer, 2010.

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4

Vidal, René, Yi Ma, and S. S. Sastry. Generalized Principal Component Analysis. Springer New York, 2016. http://dx.doi.org/10.1007/978-0-387-87811-9.

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5

J, Dunn W., Scott D. R. 1934-, and United States. Environmental Protection Agency., eds. Principal components analysis and partial least squares regression. U.S. Environmental Protection Agency, 1992.

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6

Naik, Ganesh R., ed. Advances in Principal Component Analysis. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-6704-4.

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7

Sanguansat, Parinya. Principal component analysis - multidisciplinary applications. InTech, 2012.

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8

Hyvarinen, Aapo. Independent component analysis. J. Wiley, 2001.

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9

Juha, Karhunen, and Oja Erkki, eds. Independent component analysis. J. Wiley, 2001.

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10

Kong, Xiangyu, Changhua Hu, and Zhansheng Duan. Principal Component Analysis Networks and Algorithms. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-2915-8.

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11

Suryanarayana, T. M. V., and P. B. Mistry. Principal Component Regression for Crop Yield Estimation. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0663-0.

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12

Mori, Yuichi, Masahiro Kuroda, and Naomichi Makino. Nonlinear Principal Component Analysis and Its Applications. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0159-8.

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13

D, Mobley Curtis, ed. Principal component analysis in meteorology and oceanography. Elsevier, 1988.

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14

Binongo, José Nilo G. Stylometry and its implementation by principal component analysis. The Author], 2000.

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15

Tanaka-Yamawaki, Mieko, and Yumihiko Ikura. Principal Component Analysis and Randomness Test for Big Data Analysis. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-3967-9.

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16

Rijckevorsel, Jan L. A. van. and Leeuw Jan de, eds. Component and correspondence analysis: Dimension reductionby functional approximation. Wiley, 1988.

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17

Rijckevorsel, Jan L. A. van. and Leeuw Jan de, eds. Component and correspondence analysis: Dimension reduction by functional approximation. Wiley, 1988.

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18

Checchi, Daniele. Economic interdependence and structural change: Some results from principal component analysis. Dept. of Economics and Government, Newcastle upon Tyne Polytechnic, 1989.

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19

Niesing, Jan. Simultaneous component and factor analysis methods for two or more groups: A comparative study. DSWO Press, Leiden University, 1997.

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20

Archibald, T. W. Application of principal component analysis to an aggregate stochastic dynamic programming model of multiple reservoir systems. University of Edinburgh, Management School, 1995.

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21

Eder, B. K. The spatial and temporal analysis of non-urban ozone concentrations over the eastern United States using rotated principal component analysis. U.S. Environmental Protection Agency, 1992.

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22

Eder, B. K. The spatial and temporal analysis of non-urban ozone concentrations over the eastern United States using rotated principal component analysis. U.S. Environmental Protection Agency, 1992.

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23

Akiode, Mofoluke. Factor Analysis Using the Principal Component (PCA) Method in SPSS With Data From CGAP Smallholder Household Survey (2016). SAGE Publications Ltd, 2025. https://doi.org/10.4135/9781036216504.

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24

Hall, Peter. Principal component analysis for functional data. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.8.

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This article discusses the methodology and theory of principal component analysis (PCA) for functional data. It first provides an overview of PCA in the context of finite-dimensional data and infinite-dimensional data, focusing on functional linear regression, before considering the applications of PCA for functional data analysis, principally in cases of dimension reduction. It then describes adaptive methods for prediction and weighted least squares in functional linear regression. It also examines the role of principal components in the assessment of density for functional data, showing how principal component functions are linked to the amount of probability mass contained in a small ball around a given, fixed function, and how this property can be used to define a simple, easily estimable density surrogate. The article concludes by explaining the use of PCA for estimating log-density.

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25

Soilihi, Nizar. Data Analysis: New on Functional Principal Component Analysis and on Functional Linear Regression Modeling. Independently Published, 2020.

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26

Sanguansat, Parinya, ed. Principal Component Analysis. InTech, 2012. http://dx.doi.org/10.5772/2340.

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27

Principal Component Analysis. University of Illinois, 2016. http://dx.doi.org/10.4135/9781529773163.

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28

Principal Component Analysis. Springer-Verlag, 2002. http://dx.doi.org/10.1007/b98835.

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29

Principal Component Analysis. Springer London, Limited, 2006.

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30

Principal component analysis. Springer-Verlag, 1986.

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31

Principal component analysis. 2nd ed. Springer, 2002.

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32

Principal Component Analysis. Springer London, Limited, 2013.

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33

Principal Component Analysis. IntechOpen, 2012.

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34

Sastry, Shankar, Yi Ma, and René Vidal. Generalized Principal Component Analysis. Springer, 2018.

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35

Sastry, Shankar, Yi Ma, and René Vidal. Generalized Principal Component Analysis. Springer, 2016.

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36

Sastry, Shankar, Yi Ma, and René Vidal. Generalized Principal Component Analysis. Springer London, Limited, 2016.

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37

Sastry, Shankar, Yi Ma, and René Vidal. Generalized Principal Component Analysis. Springer, 2016.

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38

Principal Component Analysis [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.97992.

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39

Sanguansat, Parinya, ed. Principal Component Analysis - Engineering Applications. InTech, 2012. http://dx.doi.org/10.5772/2693.

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40

Sanguansat, Parinya, ed. Principal Component Analysis - Multidisciplinary Applications. InTech, 2012. http://dx.doi.org/10.5772/2694.

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41

Márquez, Fausto Pedro García. Advances in Principal Component Analysis. IntechOpen, 2022.

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42

Ferraty, Frédéric, and Yves Romain, eds. The Oxford Handbook of Functional Data Analysis. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.001.0001.

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This handbook presents the state-of-the-art of the statistics dealing with functional data analysis. With contributions from international experts in the field, it discusses a wide range of the most important statistical topics (classification, inference, factor-based analysis, regression modeling, resampling methods, time series, random processes) while also taking into account practical, methodological, and theoretical aspects of the problems. The book is organised into three sections. Part I deals with regression modeling and covers various statistical methods for functional data such as linear/nonparametric functional regression, varying coefficient models, and linear/nonparametric functional processes (i.e. functional time series). Part II considers related benchmark methods/tools for functional data analysis, including curve registration methods for preprocessing functional data, functional principal component analysis, and resampling/bootstrap methods. Finally, Part III examines some of the fundamental mathematical aspects of the infinite-dimensional setting, with a focus on the stochastic background and operatorial statistics: vector-valued function integration, spectral and random measures linked to stationary processes, operator geometry, vector integration and stochastic integration in Banach spaces, and operatorial statistics linked to quantum statistics.

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43

Oja, Erkki, Aapo Hyvärinen, and Juha Karhunen. Independent Component Analysis. Wiley-Interscience, 2001.

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44

Kong, Xiangyu, Changhua Hu, and Zhansheng Duan. Principal Component Analysis Networks and Algorithms. Springer, 2016.

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45

Kong, Xiangyu, Changhua Hu, and Zhansheng Duan. Principal Component Analysis Networks and Algorithms. Springer, 2017.

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46

Kong, Xiangyu, Changhua Hu, and Zhansheng Duan. Principal Component Analysis Networks and Algorithms. Springer, 2018.

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47

Kong, Xiangyu, Changhua Hu, and Zhansheng Duan. Principal Component Analysis Networks and Algorithms. Springer, 2017.

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48

Suryanarayana, T. M. V., and P. B. Mistry. Principal Component Regression for Crop Yield Estimation. Springer, 2016.

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49

Suryanarayana, T. M. V., and P. B. Mistry. Principal Component Regression for Crop Yield Estimation. Springer, 2016.

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50

Principal Component Analysis: Methods, Applications and Technology. Nova Science Publishers, Incorporated, 2017.

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