Elements of Large-Sample Theory

  • E. L. Lehmann

Part of the Springer Texts in Statistics book series (STS)

Table of contents

  1. Front Matter
    Pages i-xii
  2. Pages 219-276
  3. Pages 277-362
  4. Pages 363-449
  5. Back Matter
    Pages 571-632

About this book

Introduction

Elements of Large-Sample Theory provides a unified treatment of first- order large-sample theory. It discusses a broad range of applications including introductions to density estimation, the bootstrap, and the asymptotics of survey methodology. The book is written at an elementary level and is suitable for students at the master's level in statistics and in aplied fields who have a background of two years of calculus.

E.L. Lehmann is Professor of Statistics Emeritus at the University of California, Berkeley. He is a member of the National Academy of Sciences and the American Academy of Arts and Sciences, and the recipient of honorary degrees from the University of Leiden, The Netherlands, and the University of Chicago.

Also available:

Lehmann/Casella, Theory at Point Estimation, 2nd ed. Springer-Verlag New York, Inc., 1998, ISBN 0- 387-98502-6

Lehmann, Testing Statistical Hypotheses, 2nd ed. Springer-Verlag New York, Inc., 1997, ISBN 0-387-94919-4

Keywords

Calc DEX Large Sample Methods Mathematica Statistica calculus convergence estimator form large Sample Theory performance probability statistics testing university

Editors and affiliations

  • E. L. Lehmann
    • 1
  1. 1.Department of StatisticsUniversity of CaliforniaBerkeleyUSA

Bibliographic information

  • DOI https://doi.org/10.1007/b98855
  • Copyright Information Springer-Verlag New York, Inc. 1999
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-0-387-98595-4
  • Online ISBN 978-0-387-22729-0
  • Series Print ISSN 1431-875X
  • About this book