Asymptotic Theory of Statistics and Probability (Springer Texts in Statistics) - Softcover

DasGupta, Anirban

Softcover

ISBN 10: 1461498848 ISBN 13: 9781461498841

Publisher: Springer, 2014

Synopsis

This unique book provides unmatched coverage of topics of interest to a very broad spectrum of statisticians and also probabilists. The book can be used in multiple roles. It can be used as a graduate text with a huge choice of topics for the instructor, as an invaluable general purpose reference, for independent reading by students and researchers, and for getting an overview of the latest developments in some of the most contemporary topics, such as false discovery, treatment of dependent data, and the bootstrap. There is no other book in large sample theory that matches this book in coverage, exercises and examples, bibliography, and lucid conceptual discussion of issues and theorems.

About this title

From the Back Cover

This book is an encyclopedic treatment of classic as well as contemporary large sample theory, dealing with both statistical problems and probabilistic issues and tools. It is written in an extremely lucid style, with an emphasis on the conceptual discussion of the importance of a problem and the impact and relevance of the theorems. The book has 34 chapters over a wide range of topics, nearly 600 exercises for practice and instruction, and another 300 worked out examples. It also includes a large compendium of 300 useful inequalities on probability, linear algebra, and analysis that are collected together from numerous sources, as an invaluable reference for researchers in statistics, probability, and mathematics.

It can be used as a graduate text, as a versatile research reference, as a source for independent reading on a wide assembly of topics, and as a window to learning the latest developments in contemporary topics. The book is unique in its detailed coverage of fundamental topics such as central limit theorems in numerous setups, likelihood based methods, goodness of fit, higher order asymptotics, as well as of the most modern topics such as the bootstrap, dependent data, Bayesian asymptotics, nonparametric density estimation, mixture models, and multiple testing and false discovery. It provides extensive bibliographic references on all topics that include very recent publications.

Anirban DasGupta is Professor of Statistics at Purdue University. He has also taught at the Wharton School of the University of Pennsylvania, at Cornell University, and at the University of California at San Diego. He has been on the editorial board of the Annals of Statistics since 1998 and has also served on the editorial boards of the Journal of the American Statistical Association, International Statistical Review, and the Journal of Statistical Planning and Inference. He has edited two monographs in the lecture notes monograph series of the Institute of Mathematical Statistics, is a Fellow of the Institute of Mathematical Statistics and has 70 refereed publications on theoretical statistics and probability in major journals.

Edition details

• Publisher: Springer
• Publication date: 2014
• ISBN 10: 1461498848
• ISBN 13: 9781461498841
• Binding: Paperback
• Language: English
• Number of pages: 749