Applied Multivariate Statistical Analysis

Wolfgang Karl Härdle is the Ladislaus von Bortkiewicz Professor of Statistics at the Humboldt-Universität zu Berlin, Germany. 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 modern machine learning, multivariate statistics in finance and computational statistics. He is an elected member of the ISI (International Statistical Institute) and the director of the Institute of Digital Assets, Academy of Economic Sciences, Bucharest, Romania.

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 methods and 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.

Matthias R. Fengler is a Professor of Econometrics at the School of Economics and Political Science at the University of St. Gallen, Switzerland. His area of specialization is Financial Econometrics and he works in asset pricing, volatility modeling, risk-management, and the analysis of financial text data. In collaboration with colleagues from both academia and industry, he initiated the University of St. Gallen's Impact Award-winning project Monitoring Consumption Switzerland, which analyzes and explores private debit and credit card expenditures and payment behavior in Switzerland.

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.- 21 Locally Linear Embedding.- 22 Stochastic Neighborhood Embedding.- 23 Uniform Manifold Approximation and Projection.- Part IV Appendix.- A Symbols and Notations.- B Data.- Index.