Includes bibliographical references and index.

I Descriptive Techniques: Comparison of Batches -- II Multivariate R andom Variables: A Short Excursion into Matrix Algebra -- Moving to Hig her Dimensions -- Multivariate Distributions -- Theory of the Multinorm al -- Theory of Estimation -- Hypothesis Testing -- III Multivariate Te chniques: Regression Models -- Variable Selection -- Decomposition of D ata Matrices by Factors -- Principal Components Analysis -- Factor Anal ysis -- Cluster Analysis -- Discriminant Analysis -- Correspondence Ana lysis -- Canonical Correlation Analysis -- Multidimensional Scaling -- Conjoint Measurement Analysis -- Applications in Finance -- Computation ally Intensive Techniques -- IV Appendix: Symbols and Notations -- Data .

Focusing on high-dimensional applications, this 4th edition presents the tools and concepts used in multivariate data analysis in a style t hat is also accessible for non-mathematicians and practitioners. It sur veys the basic principles and emphasizes both exploratory and inferenti al statistics; a new chapter on Variable Selection (Lasso, SCAD and Ela stic Net) has also been added. All chapters include practical exercises that highlight applications in different multivariate data analysis fi elds: in quantitative financial studies, where the joint dynamics of as sets are observed; in medicine, where recorded observations of subjects in different locations form the basis for reliable diagnoses and medic ation; and in quantitative marketing, where consumers' preferences are collected in order to construct models of consumer behavior. All of the se examples involve high to ultra-high dimensions and represent a numbe r 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 fo und on www.quantlet.de The practical exercises include solutions that c an be found in Hèardle, W. and Hlavka, Z., Multivariate Statistics: Exe rcises and Solutions. Springer Verlag, Heidelberg.