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Multivariate Statistics:

Exercises and Solutions

  • Textbook
  • © 2007

Overview

Authors:
  1. Wolfgang Härdle

    1. Humboldt-Universitat zu Berlin, Berlin, Germanny

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    You can also search for this author in PubMed   Google Scholar

  2. Zdeněk Hlávka

    1. Charles University in Prague, Prague, Czech Republic

    View author publications

    You can also search for this author in PubMed   Google Scholar

  • Considers techniques like Conjoint Measurement Analyses, Applications to Finance, Projection Pursuit and SIR techniques that are not found typically in multivariate textbooks
  • Data sets discussed in the book can be downloaded and analyzed by every statistical package
  • Online version powered by XploRe allows immediate calculation of formulae

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Table of contents (18 chapters)

  1. Front Matter

    Pages i-xxv
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  2. Descriptive Techniques

    1. Front Matter

      Pages 14-14
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    2. Comparison of Batches

      Pages 15-30

  3. Multivariate Random Variables

    1. Front Matter

      Pages 32-32
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    2. A Short Excursion into Matrix Algebra

      Pages 33-37

    3. Moving to Higher Dimensions

      Pages 39-54

    4. Multivariate Distributions

      Pages 55-80

    5. Theory of the Multinormal

      Pages 81-97

    6. Theory of Estimation

      Pages 99-110

    7. Hypothesis Testing

      Pages 111-144

  4. Multivariate Techniques

    1. Front Matter

      Pages 146-146
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    2. Decomposition of Data Matrices by Factors

      Pages 147-162

    3. Principal Component Analysis

      Pages 163-183

    4. Factor Analysis

      Pages 185-204

    5. Cluster Analysis

      Pages 205-225

    6. Discriminant Analysis

      Pages 227-240

    7. Correspondence Analysis

      Pages 241-262

    8. Canonical Correlation Analysis

      Pages 263-269

    9. Multidimensional Scaling

      Pages 271-281

    10. Conjoint Measurement Analysis

      Pages 283-290
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Keywords

About this book

There can be no question, my dear Watson, of the value of exercise before breakfast. Sherlock Holmes in “The Adventure of Black Peter” The statistical analysis of multivariate data requires a variety of techniques thatareentirelydi?erentfromtheanalysisofone-dimensionaldata.Thestudy of the joint distribution of many variables in high dimensions involves matrix techniques that are not part of standard curricula. The same is true for tra- formations and computer-intensive techniques, such as projection pursuit. The purpose of this book is to provide a set of exercises and solutions to help the student become familiar with the techniques necessary to analyze high-dimensional data. It is our belief that learning to apply multivariate statistics is like studying the elements of a criminological case. To become pro?cient, students must not simply follow a standardized procedure, they must compose with creativity the parts of the puzzle in order to see the big picture. We therefore refer to Sherlock Holmes and Dr. Watson citations as typical descriptors of the analysis. Puerile as such an exercise may seem, it sharpens the faculties of observation, and teaches one where to look and what to look for.

Reviews

“In general, I find this book particularly instructive, by discussing various techniques and analytical tools via exercises with rigorous solutions. The computer codes for computer-based exercises are available in R or XploRe languages through the Springer link web pages and from the authors’ home pages. The web links also provide access to real datasets used in the book. This is a very useful exercise book for students and instructors as well as for nonexperts using in applied multivariate data analysis. There has been large demand for techniques to handle and analyze high-dimensional data. In this regard, the book would be a good reference for researchers and students working in the theory or applications of multivariate statistical analysis.” (Journal of the American Statistical Association, Dec. 2009, Vol. 104, No. 488)

Authors and Affiliations

  • Humboldt-Universitat zu Berlin, Berlin, Germanny

    Wolfgang Härdle

  • Charles University in Prague, Prague, Czech Republic

    Zdeněk Hlávka

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