Applied Multivariate Statistical Analysis

• Κύριοι συγγραφείς: Härdle, Wolfgang Karl (Συγγραφέας, http://id.loc.gov/vocabulary/relators/aut), Simar, Léopold (http://id.loc.gov/vocabulary/relators/aut)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2019.
• Έκδοση: 5th ed. 2019.
• Θέματα: Statistics .; Economics, Mathematical .; Economic theory.; Statistical Theory and Methods.; Statistics for Business, Management, Economics, Finance, Insurance.; Quantitative Finance.; Economic Theory/Quantitative Economics/Mathematical Methods.; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.

 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.