Applied Multivariate Statistical Analysis

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. 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.℗l 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.℗l 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 consumersĺl preferences are collected in order to construct models of consumer behavior.℗l 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 H©Þrdle, W. and Hlavka, Z., Multivariate Statistics: Exercises and Solutions. Springer Verlag, Heidelberg. Front Matter....Pages i-xiii Front Matter....Pages 1-1 Comparison of Batches....Pages 3-50 Front Matter....Pages 51-51 A Short Excursion into Matrix Algebra....Pages 53-77 Moving to Higher Dimensions....Pages 79-115 Multivariate Distributions....Pages 117-181 Theory of the Multinormal....Pages 183-199 Theory of Estimation....Pages 201-211 Hypothesis Testing....Pages 213-249 Front Matter....Pages 251-251 Regression Models....Pages 253-280 Variable Selection....Pages 281-304 Decomposition of Data Matrices by Factors....Pages 305-318 Principal Components Analysis....Pages 319-358 Factor Analysis....Pages 359-384 Cluster Analysis....Pages 385-405 Discriminant Analysis....Pages 407-424 Correspondence Analysis....Pages 425-442 Canonical Correlation Analysis....Pages 443-454 Multidimensional Scaling....Pages 455-472 Conjoint Measurement Analysis....Pages 473-486 Applications in Finance....Pages 487-499 Computationally Intensive Techniques....Pages 501-554 Front Matter....Pages 555-555 Symbols and Notations....Pages 557-560 Data....Pages 561-571 Back Matter....Pages 573-580