Online Public Access Catalogue (OPAC)
Library,Documentation and Information Science Division

“A research journal serves that narrow

borderland which separates the known from the unknown”

-P.C.Mahalanobis


Image from Google Jackets

Applied multivariate statistical analysis / Wolfgang Karl Hardle and Leopold Simar.

By: Contributor(s): Publication details: Berlin : Springer-Verlag, 2015.Edition: 4th edDescription: xiii, 580 p. : illustrations (some colour) ; 24 cmISBN:
  • 9783662451700
Subject(s): DDC classification:
  • 000SA.07 23 H264
Contents:
I Descriptive Techniques: 1. Comparison of Batches.- 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 -- 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 -- IV Appendix: 21. Symbols and Notations -- 22. Data -- References -- Index.
Summary: 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.
Tags from this library: No tags from this library for this title. Log in to add tags.
Holdings
Item type Current library Call number Status Date due Barcode Item holds
Books ISI Library, Kolkata 000SA.07 H264 (Browse shelf(Opens below)) Available 136668
Total holds: 0

Includes bibliographical references and index.

I Descriptive Techniques:
1. Comparison of Batches.-
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 --
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 --
IV Appendix:
21. Symbols and Notations --
22. Data --
References --
Index.

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.

There are no comments on this title.

to post a comment.
Library, Documentation and Information Science Division, Indian Statistical Institute, 203 B T Road, Kolkata 700108, INDIA
Phone no. 91-33-2575 2100, Fax no. 91-33-2578 1412, ksatpathy@isical.ac.in