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Books on the topic 'Principal Component Analysis (PCA)'

Author: Grafiati

Published: 4 June 2021

Last updated: 7 September 2023

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1

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

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2

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

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3

Principal component analysis. 2nd ed. New York: Springer, 2002.

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4

Principal component analysis. New York: Springer-Verlag, 1986.

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5

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

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6

Naik, Ganesh R., ed. Advances in Principal Component Analysis. Singapore: 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. Rijeka: InTech, 2012.

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8

Hyvarinen, Aapo. Independent component analysis. New York: J. Wiley, 2001.

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9

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

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10

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

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11

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

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12

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

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13

Constrained principal component analysis and related techniques. Boca Raton: CRC, Taylor & Francis Group, 2014.

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14

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

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15

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

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16

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

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17

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

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18

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

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19

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

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20

Archibald, T. W. Application of principal component analysis to an aggregate stochastic dynamic programming model of multiple reservoir systems. Edinburgh: 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. [Washington, D.C: 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. [Washington, D.C: U.S. Environmental Protection Agency, 1992.

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23

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

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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

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

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26

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

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27

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

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28

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

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29

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

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30

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

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31

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

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32

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

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33

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

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34

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

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35

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

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36

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

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37

Zabrodin, Anton. Financial applications of random matrix theory: a short review. Edited by Gernot Akemann, Jinho Baik, and Philippe Di Francesco. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198744191.013.40.

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This article reviews some applications of random matrix theory (RMT) in the context of financial markets and econometric models, with emphasis on various theoretical results (for example, the Marčenko-Pastur spectrum and its various generalizations, random singular value decomposition, free matrices, largest eigenvalue statistics) as well as some concrete applications to portfolio optimization and out-of-sample risk estimation. The discussion begins with an overview of principal component analysis (PCA) of the correlation matrix, followed by an analysis of return statistics and portfolio theory. In particular, the article considers single asset returns, multivariate distribution of returns, risk and portfolio theory, and nonequal time correlations and more general rectangular correlation matrices. It also presents several RMT results on the bulk density of states that can be obtained using the concept of matrix freeness before concluding with a description of empirical correlation matrices of stock returns.

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38

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

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39

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

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40

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

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41

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

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42

Zinn-Justin, Paul, and Jean-Bernard Zuber. Multivariate statistics. Edited by Gernot Akemann, Jinho Baik, and Philippe Di Francesco. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198744191.013.28.

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This article considers some classical and more modern results obtained in random matrix theory (RMT) for applications in statistics. In the classic paradigm of parametric statistics, data are generated randomly according to a probability distribution indexed by parameters. From this data, which is by nature random, the properties of the deterministic (and unknown) parameters may be inferred. The ability to infer properties of the unknown Σ (the population covariance matrix) will depend on the quality of the estimator. The article first provides an overview of two spectral statistical techniques, principal components analysis (PCA) and canonical correlation analysis (CCA), before discussing the Wishart distribution and normal theory. It then describes extreme eigenvalues and Tracy–Widom laws, taking into account the results obtained in the asymptotic setting of ‘large p, large n’. It also analyses the results for the limiting spectra of sample covariance matrices..

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43

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

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44

Mori, Yuichi, Masahiro Kuroda, and Naomichi Makino. Nonlinear Principal Component Analysis and Its Applications. Springer London, Limited, 2016.

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45

Mori, Yuichi, Masahiro Kuroda, and Naomichi Makino. Nonlinear Principal Component Analysis and Its Applications. Springer, 2016.

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46

Takane, Yoshio. Constrained Principal Component Analysis and Related Techniques. Taylor & Francis Group, 2016.

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47

Preisendorfer, Rudolph W. Principal component analysis in meteoreology and oceanography. Elsevier, 1988.

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48

Takane, Yoshio. Constrained Principal Component Analysis and Related Techniques. Taylor & Francis Group, 2013.

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49

Takane, Yoshio. Constrained Principal Component Analysis and Related Techniques. Taylor & Francis Group, 2020.

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50

Takane, Yoshio. Constrained Principal Component Analysis and Related Techniques. Taylor & Francis Group, 2016.

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