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Books on the topic 'Principal components analysis (pca)'

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Published: 4 June 2021

Last updated: 8 June 2024

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1

Dunteman, George. Principal Components Analysis. 2455 Teller Road, Newbury Park California 91320 United States of America: SAGE Publications, Inc., 1989. http://dx.doi.org/10.4135/9781412985475.

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2

Principal components analysis. Newbury Park, Calif: Sage, 1989.

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3

Dunteman, George H. Principal components analysis. Newbury Park: Sage Publications, 1989.

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4

Dunteman, George H. Principal components analysis. Newbury Park: Sage Publications, 1989.

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5

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

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6

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

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7

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

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8

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

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9

A user's guide to principal components. Hoboken, N.J: Wiley-Interscience, 2003.

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10

Jackson, J. Edward. A user's guide to principal components. New York: Wiley, 1991.

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11

LeBlanc, Michael R. Adaptive principal surfaces. Toronto: University of Toronto, Dept. of Statistics, 1991.

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12

Flury, Bernhard. Common principal components and related multivariate models. New York: Wiley, 1988.

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13

The analysis of three-way arrays by constrained PARAFAC methods. Leiden, The Netherlands: DSWO Press, Leiden University, 1993.

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14

Kiers, Henk A. L. Three-way methods for the analysis of qualitative and quantitative two-way data. Leiden, Netherlands: DSWO Press, University of Leiden, 1989.

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15

Pla, Laura E. Análisis multivariado: Método de componentes principales. Washington, D.C: Secretaría General de la Organización de los Estados Americanos, Programa Regional de Desarrollo Científico y Tecnológico, 1986.

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16

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

C, Gower J., ed. Ordination and classification. Montréal, Québec, Canada: Les Presses de l'Université de Montréal, 1986.

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19

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

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20

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

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21

Klink, Katherine. Space-time analysis of the surface wind field using vector-based principal components analysis. Elmer, N.J: C.W. Thornthwaite Associates, Laboratory of climatology, 1986.

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22

Klink, Katherine. Space-time analysis of the surface wind field using vector-based principal components analysis. Elmer, N.J: C.W. Thornthwaite Associates, Laboratory of Climatology, 1986.

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23

Pentecost, Eric. A principal components analysis of financial integration within the European Community. Loughborough: Department of Economics, Loughborough University of Technology, 1991.

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24

1969-, Ye Hao, ed. Zhu yuan fen xi yu pian zui xiao er cheng fa. Beijing: Qing hua da xue chu ban she, 2012.

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25

Wightman, Frederic. Psychophysical evaluation of three-dimensional auditory displays: Semiannual progress report. [Washington, DC]: National Aeronautics and Space Administration, 1991.

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26

DeLombard, Richard. Comparison tools for assessing the microgravity environment of orbital missions, carriers and conditions. [Cleveland, Ohio]: National Aeronautics and Space Administration, Glenn Research Center, 1999.

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27

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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28

Röhr, Michael. Statistische Strukturanalysen. Stuttgart: G. Fischer, 1993.

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29

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

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30

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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31

Sinclair-Desgagne, Bernard. The first-order approach to multi-task principal-agent problems. Fontainebleau: INSEAD, 1991.

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32

Flury, Bernhard. Commonprincipal components and related multivariate models. New York: Wiley, 1988.

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33

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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34

Christian, Kesteloot, ed. Principale componentenanalyse, verwante methoden en hun gebruik in de menselijke aardrijkskunde =: Analyse en composantes principales, méthodes apparentées et leur usage en géographie humaine. [Louvain]: Geografisch Instituut, Katholieke Universiteit te Leuven, 1985.

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35

Netherlands. Ministerie van Landbouw en Visserij. Agricultural Mathematics Group. Canoco: A FORTRAN program for canonical community ordination by [partial] [detrended] [canonical] correspondence, analysis, principal components analysis and redundancy analysis (version 2.1). Wageningen: GLW, 1988.

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36

Egginton, Donald Michael. A principal components analysis of the UK term structure and the influence of fiscal policy. [s.l.]: typescript, 1999.

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37

N, Gorbanʹ A., ed. Principal manifolds for data visualization and dimension reduction. Berlin: Springer, 2007.

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38

Ahmed, S. E. (Syed Ejaz), 1957- editor of compilation, ed. Perspectives on big data analysis: Methodologies and applications : International Workshop on Perspectives on High-Dimensional Data Anlaysis II, May 30-June 1, 2012, Centre de Recherches Mathématiques, University de Montréal, Montréal, Québec, Canada. Providence, Rhode Island: American Mathematical Society, 2014.

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39

E, Vance Dennis, and Vance Jean E, eds. Biochemistry of lipids, lipoproteins, and membranes. Amsterdam: Elsevier, 1991.

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40

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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41

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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42

Jackson, J. Edward. User's Guide to Principal Components. Wiley & Sons, Incorporated, John, 2005.

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43

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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44

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

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45

Blokdyk, Gerardus. Principal Components Analysis a Complete Guide - 2020 Edition. Emereo Pty Limited, 2020.

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46

Kroonenberg, Pieter M. Applied Multiway Data Analysis. Wiley & Sons, Incorporated, John, 2008.

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47

Applied Multiway Data Analysis. Wiley-Interscience, 2008.

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48

Kroonenberg, Pieter M. Applied Multiway Data Analysis. Wiley & Sons, Incorporated, John, 2007.

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49

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

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50

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

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