Bibliografía: p. 573-576.

Pte.1. Descriptive techniques: 1. Comparison of batches -- Pte.2. 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 -- Pte.3. 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 -- Pte.4. Appendix: 21. Symbols and notations -- 22. Data.

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. All chapters include practical exercises that highlight applications in different multivariate data analysis fields. All of the 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.