Self-Normalized Processes: Limit Theory and Statistical Applications (Probability and Its Applications) - Hardcover

Críticas

From the reviews:

"Readership: Research workers in applied probability. ... it serves as a reference text for a special-topic course for PhD students; each chapter after the first ends with a collection of problems and the material is based on such a course taught by two of the authors at Stanford and Hong kong. ... It is a thorough ... study of an area of applied probability that underlies important statistical methodology. ... I am sure that the text will encourage others to join them in their work." (Martin Crowder, International Statistical Review, Vol. 77 (3), 2009)

"The monograph will certainly be of great use as a reference text for researchers working on corresponding problems, but also for Ph.D. and other advanced students who want to learn about the techniques and relevant topics in an interesting and active research area. ... this monograph provides a very useful collection of recent and earlier research results in the theory and applications of self-normalized processes and can be used as a standard reference text by graduate students and researchers in the field." (Josef Steinebach, Zentralblatt MATH, Vol. 1165, 2009)

“This book covers recent developments on self-normalized processes, emphasizing important advances in the area. It is the first book that systematically treats the theory and applications of self-normalized processes. ... In all aspects, this is an excellent book, and it is ideal for a second-year Ph.D. level topics course. It is also a great book for anyone who is interested in research in self-normalized processes and related areas.” (Fuchang Gao, Mathematical Reviews, Issue 2010 d)

Reseña del editor

Self-normalized processes are of common occurrence in probabilistic and statistical studies. A prototypical example is Student's t-statistic introduced in 1908 by Gosset, whose portrait is on the front cover. Due to the highly non-linear nature of these processes, the theory experienced a long period of slow development. In recent years there have been a number of important advances in the theory and applications of self-normalized processes. Some of these developments are closely linked to the study of central limit theorems, which imply that self-normalized processes are approximate pivots for statistical inference.

The present volume covers recent developments in the area, including self-normalized large and moderate deviations, and laws of the iterated logarithms for self-normalized martingales. This is the first book that systematically treats the theory and applications of self-normalization.