Applied Multivariate Statistical Analysis (5^th Ed., 5th ed. 2019)

This textbook presents the tools and concepts used in multivariate data analysis in a style accessible for non-mathematicians and practitioners. All chapters include practical exercises that highlight applications in different multivariate data analysis fields, and all the examples involve high to ultra-high dimensions and represent a number of major fields in big data analysis.

For this new edition, the book has been updated and extensively revised and now includes an extended chapter on cluster analysis. All solutions to the exercises are supplemented by R and MATLAB or SAS computer code and can be downloaded from the Quantlet platform. Practical exercises from this book and their solutions can also be found in the accompanying Springer book by W.K. Härdle and Z. Hlávka: Multivariate Statistics - Exercises and Solutions.

The Quantlet platform, quantlet.de, quantlet.com, quantlet.org, is an integrated QuantNet environment consisting of different types of statistics-related documents and program codes. Its goal is to promote reproducibility and offer a platform for sharing validated knowledge native to the social web. QuantNet and the corresponding data-driven document-based visualization allow readers to reproduce the tables, pictures and calculations presented in this Springer book.

Part I Descriptive Techniques.- 1 Comparison of Batches.- Part II Multivariate Random Variables.- 2 A Short Excursion into Matrix Algebra.- 3 Moving to Higher Dimensions.- 4 Multivariate Distributions.- 5 Theory of the Multinormal.- 6 Theory of Estimation.- 7 Hypothesis Testing.- Part III Multivariate Techniques.- 8 Regression Models.- 9 Variable Selection.-10 Decomposition of Data Matrices by Factors.- 11 Principal Components Analysis.- 12 Factor Analysis.- 13 Cluster Analysis.- 14 Discriminant Analysis.- 15 Correspondence Analysis.- 16 Canonical Correlation Analysis.- 17 Multidimensional Scaling.- 18 Conjoint Measurement Analysis.- 19 Applications in Finance.- 20 Computationally Intensive Techniques.- Part IV Appendix.- A Symbols and Notations.- B Data.- Index.- References.

Wolfgang Karl Härdle is the Ladislaus von Bortkiewicz Emeritus Professor of Statistics at the Humboldt-Universität zu Berlin, Germany. He is the spokesperson and coordinator of the IRTG 1792 “High Dimensional Non-stationary Time Series”. He is also a Professor at the Faculty of Mathematics and Physics at the Charles University in Prague, Czech Republic. He teaches quantitative finance and semi-parametric statistics. His research focuses on dynamic factor models, multivariate statistics in finance and computational statistics. He is an elected member of the ISI (International Statistical Institute) and advisor to WISE, Xiamen University, China.

Léopold Simar is an Emeritus Professor of Statistics at UCLouvain, Louvain-la-Neuve, Belgium. He has taught mathematical statistics, multivariate analysis, bootstrap methods in statistics and econometrics at several European universities. His research focuses on non-parametric and semi-parametric methodsand bootstrap techniques in statistics and econometrics. He is an elected member of the ISI and the past president of the Belgian Statistical Society, and is a regular Visiting Professor at the Sapienza University of Rome, Italy and at the Toulouse School of Economics, France.