Applied Multivariate Statistical Analysis, 4th Edition

Focusing on high-dimensional applications, this 4th edition presents the tools and concepts used in multivariate data analysis in a style that is also accessible for non-mathematicians and practitioners.  It surveys the basic principles and emphasizes both exploratory and inferential statistics; a new chapter on Variable Selection (Lasso, SCAD and Elastic Net) has also been added.  All chapters include practical exercises that highlight applications in different multivariate data analysis fields: in quantitative financial studies, where the joint dynamics of assets are observed; in medicine, where recorded observations of subjects in different locations form the basis for reliable diagnoses and medication; and in quantitative marketing, where consumers’ preferences are collected in order to construct models of consumer behavior.  All of these examples involve high to ultra-high dimensions and represent a number of major fields in big data analysis.

The fourth edition of this book on Applied Multivariate Statistical Analysis offers the following new features:

A new chapter on Variable Selection (Lasso, SCAD and Elastic Net)

All exercises are supplemented by R and MATLAB code that can be found on www.quantlet.de.

The practical exercises include solutions that can be found in Härdle, W. and Hlavka, Z., Multivariate Statistics: Exercises and Solutions. Springer Verlag, Heidelberg.

Table of Contents

Part I Descriptive Techniques
Chapter 1 Comparison of Batches

Part II Multivariate Random Variables
Chapter 2 A Short Excursion into Matrix Algebra
Chapter 3 Moving to Higher Dimensions
Chapter 4 Multivariate Distributions
Chapter 5 Theory of the Multinormal
Chapter 6 Theory of Estimation
Chapter 7 Hypothesis Testing

Part III Multivariate Techniques
Chapter 8 Regression Models
Chapter 9 Variable Selection
Chapter 10 Decomposition of Data Matrices by Factors
Chapter 11 Principal Components Analysis
Chapter 12 Factor Analysis
Chapter 13 Cluster Analysis
Chapter 14 Discriminant Analysis
Chapter 15 Correspondence Analysis
Chapter 16 Canonical Correlation Analysis
Chapter 17 Multidimensional Scaling
Chapter 18 Conjoint Measurement Analysis
Chapter 19 Applications in Finance
Chapter 20 Computationally Intensive Techniques

Part IV Appendix
Chapter 21 Symbols and Notations
Chapter 22 Data