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Robust Multivariate Analysis, 1st ed. 2017
◆Springer Yellow セール開催中！：2025年6月29日(日)ご注文分まで
※上記表示の販売価格は割引適用後の価格です 出版済み 3週間でお届けいたします。 Author: J. Olive, David Publisher: Springer ISBN: 9783319682518 Cover: HARDCOVER Date: 2017年12月 DESCRIPTION This text presents methods that are robust to the assumption of a multivariate normal distribution or methods that are robust to certain types of outliers. Instead of using exact theory based on the multivariate normal distribution, the simpler and more applicable large sample theory is given. The text develops among the first practical robust regression and robust multivariate location and dispersion estimators backed by theory. The robust techniques are illustrated for methods such as principal component analysis, canonical correlation analysis, and factor analysis. A simple way to bootstrap confidence regions is also provided. Much of the research on robust multivariate analysis in this book is being published for the first time. The text is suitable for a first course in Multivariate Statistical Analysis or a first course in Robust Statistics. This graduate text is also useful for people who are familiar with the traditional multivariate topics, but want to know more about handling data sets with outliers. Many R programs and R data sets are available on the author’s website. TABLE OF CONTENTS Introduction Multivariate Distributions Elliptically Contoured Distributions MLD Estimators DD Plots and Prediction Regions Principal Component Analysis Canonical Correlation Analysis Discriminant Analysis Hotelling’s T2 Test MANOVA Factor Analysis Multivariate Linear Regression Clustering Other Techniques Stuff for Students
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