Applied multivariate statistical analysis
Author(s)
Bibliographic Information
Applied multivariate statistical analysis
Springer, c2015
4th ed
Available at 7 libraries
Note
Previous ed.: c2012
Includes bibliographical references (p. 573-576) and index
Description and Table of Contents
Description
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. All chapters include practical exercises that highlight applications in different multivariate data analysis fields. All of the 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.
Table of Contents
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.
by "Nielsen BookData"