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Books on the topic 'Multivariate analysis technique'

Author: Grafiati

Published: 3 June 2025

Last updated: 19 July 2025

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1

Wesselman, A. M. The population-sample decomposition method: A distribution-free estimation technique for minimum distance parameters. M. Nijhoff, 1987.

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2

Fu, Tsu-Tan. An analysis of the potential acceptance of market alternatives by U.S. peanut producers using a multivariate probit system of equations technique. Institute of Economics, Academia Sinica, 1988.

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3

Hasse Workshop (4th 2001 Iffeldorf, Germany). Order theoretical tools in environmental sciences: Order theory (Hasse diagram technique) meets multivariate statistics, proceedings of the fourth workshop November 05-06 2001 in Iffeldorf, Bavaria, Germany. Shaker Verlag, 2002.

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4

Tatsuoka, Maurice M. Multivariate analysis: Techniques for educational and psychological research. 2nd ed. Macmillan, 1988.

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5

Sharma, Subhash. Applied multivariate techniques. J. Wiley, 1996.

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6

Sundararajan, Srikrishnan. Multivariate Analysis and Machine Learning Techniques. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-99-0353-5.

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7

Collet, Christophe. Multivariate image processing. ISTE, 2009.

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8

Collet, Christophe. Multivariate image processing. ISTE, 2009.

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9

Hans, Grahn, and Geladi Paul, eds. Techniques and applications of hyperspectral image analysis. J. Wiley, 2007.

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10

Izenman, Alan Julian. Modern multivariate statistical techniques: Regression, classification, and manifold learning. Springer, 2008.

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11

Baxter, M. J. Exploratory multivariate analysis in archaeology. Edinburgh University Press, 1994.

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12

Biddle, Stuart. Quantitative data analysis: An introduction to multivariate statistical techniques. University of Exeter, School of Education, Research Support Unit, 1995.

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13

A, Marcoulides George, and Schumacker Randall E, eds. Advanced structural equation modeling: Issues and techniques. L. Erlbaum Associates, 1996.

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14

Shaw, Gareth. Statistical techniques in geographical analysis. 2nd ed. David Fulton Publishers, 1994.

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15

Shaw, Gareth. Statistical techniques in geographical analysis. 2nd ed. David Fulton, 1994.

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16

Breadon, J. A. M. The application and comparison of multivariate techniques in the analysis of company data. The Author], 1995.

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17

Yapaskurt, Oleg, and Evgeniya Karpova. Stadial analysis of lithogenesis. INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/21034.

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The tutorial deals with modern methods stadial analysis which allows you to retroactively recreate the stages of the successive changes in the substance of sludge as its transformation into the breed and during the stay of the breed in stratisphere. Seems some of the techniques of deciphering and mineral structural and textural transformations, corresponding to different stages of lithogenesis and early metamorphism using optical and electron microscopy, as well as methods of synthesis of such observations on genetic (lithofacies and formational) basis, theoretical understanding of the mechani

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18

Zamora, Carmen Mun̂oz. Evaluation de la depression socio-économique et delimitation des zones défavorisées en Navarre (Espagne): Une application des techniques d'analyse multivariable. Institut agronomique méditerranéen de Montpellier, 1986.

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19

ICA 2004 (2004 Granada, Spain). Independent component analysis and blind signal separation: Fifth international conference, ICA 2004, Granada, Spain, September 22-24, 2004 : proceedings. Springer, 2004.

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20

universitet, Stockholms, ed. Polycyclic aromatic compounds (PAC) in diesel fuel and exhaust emissions: Aspects of sampling techniques, chemical determination, multivariate data interpretation and PAC emissions reduction. Arrhenius Laboratory, Dept. of Analytical Chemistry, Stockholm University, 1993.

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21

Categorical and Nonparametric Data Analysis: Choosing the Best Statistical Technique. Taylor & Francis Group, 2014.

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22

Nussbaum, E. Michael. Categorical and Nonparametric Data Analysis: Choosing the Best Statistical Technique. Taylor & Francis Group, 2014.

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23

Nussbaum, E. Michael. Categorical and Nonparametric Data Analysis: Choosing the Best Statistical Technique. Taylor & Francis Group, 2014.

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24

Nussbaum, E. Michael. Categorical and Nonparametric Data Analysis: Choosing the Best Statistical Technique. Taylor & Francis Group, 2014.

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25

Nussbaum, E. Michael. Categorical and Nonparametric Data Analysis: Choosing the Best Statistical Technique. Taylor & Francis Group, 2014.

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26

Nussbaum, E. Michael. Categorical and Nonparametric Data Analysis: Choosing the Best Statistical Technique. Taylor & Francis Group, 2014.

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27

Categorical and Nonparametric Data Analysis: Choosing the Best Statistical Technique. Routledge, 2014.

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28

Categorical and Nonparametric Data Analysis: Choosing the Best Statistical Technique. Taylor & Francis Group, 2014.

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29

The Chicago Guide to Writing about Multivariate Analysis (Chicago Guides to Writing, Editing, and Publishing). University Of Chicago Press, 2005.

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30

The Chicago Guide to Writing about Multivariate Analysis (Chicago Guides to Writing, Editing, and Publishing). University Of Chicago Press, 2005.

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31

Applied Statistics II: Multivariable and Multivariate Techniques. SAGE Publications, Incorporated, 2020.

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32

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

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33

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

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34

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

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35

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

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36

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

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37

Introduction to Multivariate Statistical Analysis in Chemometrics. CRC, 2008.

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38

Filzmoser, Peter, and Kurt Varmuza. Introduction to Multivariate Statistical Analysis in Chemometrics. Taylor & Francis Group, 2016.

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39

Filzmoser, Peter, and Kurt Varmuza. Introduction to Multivariate Statistical Analysis in Chemometrics. Taylor & Francis Group, 2009.

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40

Martens, Harald, and Tormod Næs. Multivariate Calibration. Wiley, 1992.

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41

(Editor), George A. Marcoulides, and Randall E. Schumacker (Editor), eds. New Developments and Techniques in Structural Equation Modeling. Lawrence Erlbaum, 2001.

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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 technique

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43

Geladi, Paul, and Hans Grahn. Techniques and Applications of Hyperspectral Image Analysis. Wiley & Sons, Incorporated, John, 2007.

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44

Geladi, Paul, and Hans Grahn. Techniques and Applications of Hyperspectral Image Analysis. Wiley & Sons, Incorporated, John, 2007.

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45

Geladi, Paul, and Hans Grahn. Techniques and Applications of Hyperspectral Image Analysis. Wiley & Sons, Incorporated, John, 2007.

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46

Vidales, A. Data Science with Matlab. Multivariate Data Analysis Techniques. Independently Published, 2019.

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47

Whalley, R. Application of Multivariable System Techniques (AMST '94). Wiley, 1994.

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48

Tacq, Jacques. Multivariate Analysis Techniques in Social Science Research: From Problem to Analysis. Sage Publications Ltd, 1997.

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49

Tacq, Jacques. Multivariate Analysis Techniques in Social Science Research: From Problem to Analysis. Sage Publications Ltd, 1997.

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50

Warner, Rebecca M. Applied Statistics: From Bivariate Through Multivariate Techniques. SAGE Publications, Incorporated, 2013.

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