Nonparametric Goodness-of-Fit Testing Under Gaussian Models [Paperback]

This book presents the modern theory of nonparametric goodness-of-fit testing. It fills the gap in modern nonparametric statistical theory by discussing hypothesis testing and addresses mathematical statisticians who are interesting in the theory of non-parametric statistical inference. It will be of interest to specialists who are dealing with applied non-parametric statistical problems relevant in signal detection and transmission and in technical and medical diagnostics among others.

From the reviews:

The book is self-contained, and the bibliography is very rich and in fact provides a comprehensive listing of references about minimax testing (something that heretofore had been missing from the field.) To get the best out of this book, the reader should be familiar with basic functional analysis, wavelet theory, and optimization for extreme problems&It is highly recommended to anyone who wants an introduction to hypothesis testing from the minimax approachyet it is only a starting point, as Gaussian models are studied exclusively. Journal of the American Statistical Association, June 2004

The book deals with nonparametric goodness-of-fit testing problems from the literature of the past twenty years. & It is a theoretical book with mathematical results & . The proofs of the theorems are very detailed and many details are in the appendix of more than one hundred pages. (N. D. C. Veraverbeke, Short Book Reviews, Vol. 24 (1), 2004)

The present book is devoted to a modern theory of nonparametric goodness-of-fit testing. & The level of the book meets a quite high standard. The book will certainly be of interest to mathematical statisticians interested lă#