Applied multivariate statistical analysis
Author(s)
Bibliographic Information
Applied multivariate statistical analysis
Springer, c2012
3rd ed
Available at / 7 libraries
-
No Libraries matched.
- Remove all filters.
Note
Includes bibliographical references (p. 509-512) and index
"EXTRA MATERIALS : R & Matlab codes" -- Cover
Description and Table of Contents
Description
Most of the observable phenomena in the empirical sciences are of a multivariate nature. In financial studies, assets are observed simultaneously and their joint development is analysed to better understand general risk and to track indices. In medicine recorded observations of subjects in different locations are the basis of reliable diagnoses and medication. In quantitative marketing consumer preferences are collected in order to construct models of consumer behavior. The underlying data structure of these and many other quantitative studies of applied sciences is multivariate. Focusing on applications this book presents the tools and concepts of multivariate data analysis in a way that is understandable for non-mathematicians and practitioners who need to analyze statistical data. The book surveys the basic principles of multivariate statistical data analysis and emphasizes both exploratory and inferential statistics. All chapters have exercises that highlight applications in different fields.
The third edition of this book on Applied Multivariate Statistical Analysis offers the following new features * A new Chapter on Regression Models has been added* All numerical examples have been redone, updated and made reproducible in MATLAB or R, see www.quantlet.org for a repository of quantlets.
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.- 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.- Bibliography.- Index
by "Nielsen BookData"