Drawing on the expertise of researchers from around the world, and covering over a decade's worth of developments in the field, Robust Statistical Procedures: Asymptotics and Interrelations:

Discusses both theory and applications in its two parts, from the fundamentals to robust statistical inference
Thoroughly explores the interrelations between diverse classes of procedures, unlike any other book
Compares nonparametric procedures with robust statistics, explaining in detail asymptotic representations for various estimators
Provides a timesaving list of mathematical tools for the problems under discussion
Keeps mathematical abstractions to a minimum, in spite of its largely theoretical content
Includes useful problems and exercises at the end of each chapter
Offers strategies for more complex models when using robust statistical procedures
Self-contained and rounded in approach, this book is invaluable for both applied statisticians and theoretical researchers; for graduate students in mathematical statistics; and for anyone interested in the influence of this methodology.