03166nam a22003135i 4500003000500000005001700005008004100022020001800063020001800081041001200099082004300111100002900154100001200183245009300195250001800288300005200306505081700358506007701175520131401252538003602566538007202602650001802674650003102692650002102723700002202744700001202766776003702778776003702815cutn20241010103048.0191122s2019    gw |    s    |||| 0|eng d  a9783030260064  a9783030260057  aEnglish  2Switzerland :aSpringer, Cham, b2019.1 aHärdle, Wolfgang Karl.1 eauthor.10aApplied Multivariate Statistical Analysis /cby Wolfgang Karl Härdle, Léopold Simar.  a5th ed. 2019.  aXII, 558 p. 443 :billus., 308 illus. in color.0 aPart 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.  aOnline version restricted to NUS staff and students only through NUSNET.  aThis 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.  aMode of access: World Wide Web.  aSystem requirements: Internet connectivity; World Wide Web browser. 0aStatistics . 0aEconomics, Mathematical . 0aEconomic theory.1 aSimar, Léopold.1 eauthor.08iPrinted edition:z9783030260057.08iPrinted edition:z9783030260071.