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Applied multivariate statistical ana...

Hardle, Wolfgang Karl.

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 Hardle, Leopold Simar.
作者:	Hardle, Wolfgang Karl.
其他作者:	Simar, Leopold.
出版者:	Cham :Springer International Publishing :2019.
面頁冊數:	xii, 558 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Multivariate analysis.
電子資源:	https://doi.org/10.1007/978-3-030-26006-4
ISBN:	9783030260064$q(electronic bk.)

Applied multivariate statistical analysis
Hardle, Wolfgang Karl.

Applied multivariate statistical analysis[electronic resource] /by Wolfgang Karl Hardle, Leopold Simar. - 5th ed. - Cham :Springer International Publishing :2019. - xii, 558 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 -- Part IV Appendix -- A Symbols and Notations -- B Data -- Index -- References.

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. Hardle and Z. Hlavka: 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.

ISBN: 9783030260064$q(electronic bk.)

Standard No.: 10.1007/978-3-030-26006-4doiSubjects--Topical Terms:

181905
Multivariate analysis.

LC Class. No.: QA278 / .H2 2018

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 -- Part IV Appendix -- A Symbols and Notations -- B Data -- Index -- References.
520 $a 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. Hardle and Z. Hlavka: 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.
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