This book is a companion to [Zbl 1305.62006] by the same author. Again, it is intended to give students of different stages of experience a good deal of support for statistics in theory and practice. In the present book, the topics are ordered around the notions robustness and multivariance. It clearly distinguishes between the robustness against outliers and the robustness against deviations from an assumed normal distribution.
Page viii of the preface says: “Much of the research on robust multivariate analysis in this book is being published for the first time.” From the multivariate distributions discussed, the elliptically contoured ones play an essential role in this book.
From the publisher’s description: “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. 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.”