04431nam a22005775i 4500 978-3-031-63833-6 DE-He213 20251030113113.0 cr nn 008mamaa 240929s2024 sz | s |||| 0|eng d 9783031638336 978-3-031-63833-6 10.1007/978-3-031-63833-6 doi QA276-280 PBT bicssc MAT029000 bisacsh PBT thema Härdle, Wolfgang Karl. author. aut http://id.loc.gov/vocabulary/relators/aut 85228 Applied Multivariate Statistical Analysis [electronic resource] / by Wolfgang Karl Härdle, Léopold Simar, Matthias R. Fengler. 6th ed. 2024. Cham : Springer International Publishing : Imprint: Springer, 2024. XV, 613 p. 203 illus., 141 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda 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. Now in its sixth edition, this textbook presents the tools and concepts used in multivariate data analysis in a style accessible for non-mathematicians and practitioners. Each chapter features hands-on exercises that showcase applications across various fields of multivariate data analysis. These exercises utilize high-dimensional to ultra-high-dimensional data, reflecting real-world challenges in big data analysis. For this new edition, the book has been updated and revised and now includes new chapters on modern machine learning techniques for dimension reduction and data visualization, namely locally linear embedding, t-distributed stochastic neighborhood embedding, and uniform manifold approximation and projection, which overcome the shortcomings of traditional visualization and dimension reduction techniques. Solutions to the book’s exercises are supplemented by R and MATLAB or SAS computer code and are available online on the Quantlet and Quantinar platforms. 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. Statistics. 65982 Statistics . 65982 Social sciences Mathematics. 69736 Econometrics. 57695 Statistical Theory and Methods. Statistics in Business, Management, Economics, Finance, Insurance. 69738 Mathematics in Business, Economics and Finance. 69739 Quantitative Economics. 70330 Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. 66911 Applied Statistics. 65986 Simar, Léopold. author. aut http://id.loc.gov/vocabulary/relators/aut 85229 Fengler, Matthias R. author. aut http://id.loc.gov/vocabulary/relators/aut 85230 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031638329 Printed edition: 9783031638343 https://doi.org/10.1007/978-3-031-63833-6 ZDB-2-SMA ZDB-2-SXMS 63821 63821