All of Statistics

A Concise Course in Statistical Inference

  • Larry Wasserman

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

Table of contents

  1. Front Matter
    Pages i-xix
  2. Probability

    1. Front Matter
      Pages 1-1
    2. Larry Wasserman
      Pages 3-17
    3. Larry Wasserman
      Pages 19-46
    4. Larry Wasserman
      Pages 48-61
    5. Larry Wasserman
      Pages 63-69
    6. Larry Wasserman
      Pages 71-84
  3. Statistical Inference

    1. Front Matter
      Pages 85-85
    2. Larry Wasserman
      Pages 87-96
    3. Larry Wasserman
      Pages 97-105
    4. Larry Wasserman
      Pages 107-118
    5. Larry Wasserman
      Pages 119-148
    6. Larry Wasserman
      Pages 149-173
    7. Larry Wasserman
      Pages 175-192
    8. Larry Wasserman
      Pages 193-205
  4. Statistical Models and Methods

    1. Front Matter
      Pages 207-207
    2. Larry Wasserman
      Pages 209-229
    3. Larry Wasserman
      Pages 231-238
    4. Larry Wasserman
      Pages 239-249
    5. Larry Wasserman
      Pages 251-262
    6. Larry Wasserman
      Pages 263-279
    7. Larry Wasserman
      Pages 281-289
    8. Larry Wasserman
      Pages 291-301
    9. Larry Wasserman
      Pages 303-326
    10. Larry Wasserman
      Pages 327-348
    11. Larry Wasserman
      Pages 349-379
    12. Larry Wasserman
      Pages 381-401
    13. Larry Wasserman
      Pages 403-433
  5. Back Matter
    Pages 434-444

About this book


This book is for people who want to learn probability and statistics quickly. It brings together many of the main ideas in modern statistics in one place. The book is suitable for students and researchers in statistics, computer science, data mining and machine learning.

This book covers a much wider range of topics than a typical introductory text on mathematical statistics. It includes modern topics like nonparametric curve estimation, bootstrapping and classification, topics that are usually relegated to follow-up courses. The reader is assumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. The text can be used at the advanced undergraduate and graduate level.

Larry Wasserman is Professor of Statistics at Carnegie Mellon University. He is also a member of the Center for Automated Learning and Discovery in the School of Computer Science. His research areas include nonparametric inference, asymptotic theory, causality, and applications to astrophysics, bioinformatics, and genetics. He is the 1999 winner of the Committee of Presidents of Statistical Societies Presidents' Award and the 2002 winner of the Centre de recherches mathematiques de Montreal–Statistical Society of Canada Prize in Statistics. He is Associate Editor of The Journal of the American Statistical Association and The Annals of Statistics. He is a fellow of the American Statistical Association and of the Institute of Mathematical Statistics.


Bootstrapping Mathematica ROOT Random variable STATISTICA classification data mining machine learning mathematical statistics

Authors and affiliations

  • Larry Wasserman
    • 1
  1. 1.Department of StatisticsCarnegie Mellon UniversityPittsburghUSA

Bibliographic information

  • DOI
  • Copyright Information Springer Science+Business Media, LLC, part of Springer Nature 2004
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4419-2322-6
  • Online ISBN 978-0-387-21736-9
  • Series Print ISSN 1431-875X
  • Series Online ISSN 2197-4136
  • Buy this book on publisher's site