Applied Multivariate Statistical Analysis /
by Härdle, Wolfgang Karl.
Edition statement:4th ed. 2015. Physical details: 1 online resource (XIII, 580 pages 221 illustrations, 83 illustrations in color.) ISBN:9783662451717.| Item type | Current location | Call number | Status | Date due | Barcode |
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Book (Long Loan)
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GU Main Campus General Stacks | QA278.H472015 (Browse shelf) | Available | 22010608 |
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| QA276.25 .L56 1995 New Cambridge statistical tables / | QA276.25 .L56 1995 New Cambridge statistical tables / | QA276.25 .L56 1995 New Cambridge statistical tables / | QA278.H472015 Applied Multivariate Statistical Analysis / | QA278.2 .C5 2012 Regression analysis by example. | QA297 .J 35 Numerical Methods | QA300 .J82 2008 Mathematical techniques : |
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 Härdle, W. and Hlavka, Z., Multivariate Statistics: Exercises and Solutions. Springer Verlag, Heidelberg.
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