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Applied multivariate statistical ana...

Hardle, Wolfgang Karl.

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Applied multivariate statistical analysis

Record Type:	Electronic resources : Monograph/item
Title/Author:	Applied multivariate statistical analysis/ by Wolfgang Karl Hardle, Leopold Simar.
Author:	Hardle, Wolfgang Karl.
other author:	Simar, Leopold.
Published:	Berlin, Heidelberg :Springer Berlin Heidelberg : : 2015.,
Description:	xiii, 580 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	I Descriptive Techniques: Comparison of Batches - II Multivariate Random Variables: A Short Excursion into Matrix Algebra -- Moving to Higher Dimensions -- Multivariate Distributions -- Theory of the Multinormal -- Theory of Estimation -- Hypothesis Testing -- III Multivariate Techniques: Regression Models -- Variable Selection -- Decomposition of Data Matrices by Factors -- Principal Components Analysis -- Factor Analysis -- Cluster Analysis -- Discriminant Analysis -- Correspondence Analysis -- Canonical Correlation Analysis -- Multidimensional Scaling -- Conjoint Measurement Analysis -- Applications in Finance -- Computationally Intensive Techniques -- IV Appendix: Symbols and Notations -- Data.
Contained By:	Springer eBooks
Subject:	Multivariate analysis. -
Online resource:	http://dx.doi.org/10.1007/978-3-662-45171-7
ISBN:	9783662451717 (electronic bk.)

Applied multivariate statistical analysis
Hardle, Wolfgang Karl.

Applied multivariate statistical analysis[electronic resource] /by Wolfgang Karl Hardle, Leopold Simar. - 4th ed. - Berlin, Heidelberg :Springer Berlin Heidelberg :2015. - xiii, 580 p. :ill. (some col.), digital ;24 cm.

I Descriptive Techniques: Comparison of Batches - II Multivariate Random Variables: A Short Excursion into Matrix Algebra -- Moving to Higher Dimensions -- Multivariate Distributions -- Theory of the Multinormal -- Theory of Estimation -- Hypothesis Testing -- III Multivariate Techniques: Regression Models -- Variable Selection -- Decomposition of Data Matrices by Factors -- Principal Components Analysis -- Factor Analysis -- Cluster Analysis -- Discriminant Analysis -- Correspondence Analysis -- Canonical Correlation Analysis -- Multidimensional Scaling -- Conjoint Measurement Analysis -- Applications in Finance -- Computationally Intensive Techniques -- IV Appendix: Symbols and Notations -- Data.

Focusing on high-dimensional applications, this 4th edition presents the tools and concepts used in multivariate data analysis in a style that is also accessible for non-mathematicians and practitioners. It surveys the basic principles and emphasizes both exploratory and inferential statistics; a new chapter on Variable Selection (Lasso, SCAD and Elastic Net) has also been added. All chapters include practical exercises that highlight applications in different multivariate data analysis fields: in quantitative financial studies, where the joint dynamics of assets are observed; in medicine, where recorded observations of subjects in different locations form the basis for reliable diagnoses and medication; and in quantitative marketing, where consumers' preferences are collected in order to construct models of consumer behavior. All of these examples involve high to ultra-high dimensions and represent a number of major fields in big data analysis. The fourth edition of this book on Applied Multivariate Statistical Analysis offers the following new features: A new chapter on Variable Selection (Lasso, SCAD and Elastic Net) All exercises are supplemented by R and MATLAB code that can be found on www.quantlet.de The practical exercises include solutions that can be found in Hardle, W. and Hlavka, Z., Multivariate Statistics: Exercises and Solutions. Springer Verlag, Heidelberg.

ISBN: 9783662451717 (electronic bk.)

Standard No.: 10.1007/978-3-662-45171-7doiSubjects--Topical Terms:

517467
Multivariate analysis.

LC Class. No.: QA278

Dewey Class. No.: 519.535

Applied multivariate statistical analysis
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245 1 0 $a Applied multivariate statistical analysis $h [electronic resource] / $c by Wolfgang Karl Hardle, Leopold Simar.
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300 $a xiii, 580 p. : $b ill. (some col.), digital ; $c 24 cm.
505 0 $a I Descriptive Techniques: Comparison of Batches - II Multivariate Random Variables: A Short Excursion into Matrix Algebra -- Moving to Higher Dimensions -- Multivariate Distributions -- Theory of the Multinormal -- Theory of Estimation -- Hypothesis Testing -- III Multivariate Techniques: Regression Models -- Variable Selection -- Decomposition of Data Matrices by Factors -- Principal Components Analysis -- Factor Analysis -- Cluster Analysis -- Discriminant Analysis -- Correspondence Analysis -- Canonical Correlation Analysis -- Multidimensional Scaling -- Conjoint Measurement Analysis -- Applications in Finance -- Computationally Intensive Techniques -- IV Appendix: Symbols and Notations -- Data.
520 $a Focusing on high-dimensional applications, this 4th edition presents the tools and concepts used in multivariate data analysis in a style that is also accessible for non-mathematicians and practitioners. It surveys the basic principles and emphasizes both exploratory and inferential statistics; a new chapter on Variable Selection (Lasso, SCAD and Elastic Net) has also been added. All chapters include practical exercises that highlight applications in different multivariate data analysis fields: in quantitative financial studies, where the joint dynamics of assets are observed; in medicine, where recorded observations of subjects in different locations form the basis for reliable diagnoses and medication; and in quantitative marketing, where consumers' preferences are collected in order to construct models of consumer behavior. All of these examples involve high to ultra-high dimensions and represent a number of major fields in big data analysis. The fourth edition of this book on Applied Multivariate Statistical Analysis offers the following new features: A new chapter on Variable Selection (Lasso, SCAD and Elastic Net) All exercises are supplemented by R and MATLAB code that can be found on www.quantlet.de The practical exercises include solutions that can be found in Hardle, W. and Hlavka, Z., Multivariate Statistics: Exercises and Solutions. Springer Verlag, Heidelberg.
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