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Applied multivariate statistical analysis

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Applied multivariate statistical analysisby Wolfgang Karl Härdle, Léopold Simar, Matthias R. Fengler.
作者:	Härdle, Wolfgang.
其他作者:	Simar, Léopold.
出版者:	Cham :Springer International Publishing :2024.
面頁冊數:	xv, 613 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Multivariate analysis.
電子資源:	https://doi.org/10.1007/978-3-031-63833-6
ISBN:	9783031638336$q(electronic bk.)

Applied multivariate statistical analysis
Härdle, Wolfgang.

Applied multivariate statistical analysis[electronic resource] /by Wolfgang Karl Härdle, Léopold Simar, Matthias R. Fengler. - Sixth edition. - Cham :Springer International Publishing :2024. - xv, 613 p. :ill., digital ;24 cm.

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.

ISBN: 9783031638336$q(electronic bk.)

Standard No.: 10.1007/978-3-031-63833-6doiSubjects--Topical Terms:

181905
Multivariate analysis.

LC Class. No.: QA278

Dewey Class. No.: 519.535

Applied multivariate statistical analysis
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505 0 $a 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.
520 $a 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.
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