Publisher's Synopsis
A broad and unified methodology for robust statistics—with exciting new applications
Robust statistics is one of the fastest growing fields in contemporary statistics. It is also one of the more diverse and sometimes confounding areas, given the many different assessments and interpretations of robustness by theoretical and applied statisticians. This innovative book unifies the many varied, yet related, concepts of robust statistics under a sound theoretical modulation. It seamlessly integrates asymptotics and interrelations, and provides statisticians with an effective system for dealing with the interrelations between the various classes of procedures.
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.
