Asymptotic Theory of Statistics and Probability

  • Anirban┬áDasGupta
Part of the Springer Texts in Statistics book series (STS)

Table of contents

  1. Front Matter
    Pages I-XXVII
  2. Anirban DasGupta
    Pages 1-17
  3. Anirban DasGupta
    Pages 49-61
  4. Anirban DasGupta
    Pages 63-81
  5. Anirban DasGupta
    Pages 83-89
  6. Anirban DasGupta
    Pages 91-100
  7. Anirban DasGupta
    Pages 101-117
  8. Anirban DasGupta
    Pages 119-129
  9. Anirban DasGupta
    Pages 131-140
  10. Anirban DasGupta
    Pages 141-149
  11. Anirban DasGupta
    Pages 151-183
  12. Anirban DasGupta
    Pages 185-201
  13. Anirban DasGupta
    Pages 203-224
  14. Anirban DasGupta
    Pages 225-234
  15. Anirban DasGupta
    Pages 235-258
  16. Anirban DasGupta
    Pages 259-269
  17. Anirban DasGupta
    Pages 271-278

About this book

Introduction

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.

Keywords

Median Uniform integrability Variance best fit central limit theorems false discovery likelihood nonparametrics resampling

Authors and affiliations

  • Anirban┬áDasGupta
    • 1
  1. 1.Department of StatisticsPurdue UniversityWest Lafayette

Bibliographic information

  • DOI https://doi.org/10.1007/978-0-387-75971-5
  • Copyright Information Springer Science+Business Media, LLC 2008
  • Publisher Name Springer, New York, NY
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-0-387-75970-8
  • Online ISBN 978-0-387-75971-5
  • Series Print ISSN 1431-875X
  • About this book