All of Statistics

1. Front Matter

   Pages i-xix

   PDF

2. Probability

   1. Front Matter

      Pages 1-1

      PDF

   2. Probability

      • Larry Wasserman

      Pages 3-17

   3. Random Variables

      • Larry Wasserman

      Pages 19-46

   4. Expectation

      • Larry Wasserman

      Pages 48-61

   5. Inequalities

      • Larry Wasserman

      Pages 63-69

   6. Convergence of Random Variables

      • Larry Wasserman

      Pages 71-84

3. Statistical Inference

   1. Front Matter

      Pages 85-85

      PDF

   2. Models, Statistical Inference and Learning

      • Larry Wasserman

      Pages 87-96

   3. Estimating the CDF and Statistical Functionals

      • Larry Wasserman

      Pages 97-105

   4. The Bootstrap

      • Larry Wasserman

      Pages 107-118

   5. Parametric Inference

      • Larry Wasserman

      Pages 119-148

   6. Hypothesis Testing and p-values

      • Larry Wasserman

      Pages 149-173

   7. Bayesian Inference

      • Larry Wasserman

      Pages 175-192

   8. Statistical Decision Theory

      • Larry Wasserman

      Pages 193-205

4. Statistical Models and Methods

   1. Front Matter

      Pages 207-207

      PDF

   2. Linear and Logistic Regression

      • Larry Wasserman

      Pages 209-229

   3. Multivariate Models

      • Larry Wasserman

      Pages 231-238

   4. Inference About Independence

      • Larry Wasserman

      Pages 239-249

   5. Causal Inference

      • Larry Wasserman

      Pages 251-262

Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. 

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

Winner of the 2005 DeGroot Prize.

From the reviews:

"Presuming no previous background in statistics and described by the author as "demanding" yet "understandable because the material is as intuitive as possible" (p. viii), this certainly would be my choice of textbook if I was required to learn mathematical statistics again for a couple of semesters." Technometrics, August 2004

"This book should be seriously considered as a text for a theoretical statsitics course for non-majors, and perhaps even for majors...The coverage of emerging and important topics is timely and welcomed...you should have this book on your desk as a reference to nothing less than 'All of Statistics.'" Biometrics, December 2004

"Although All of Statistics is an ambitious title, this book is a concise guide, as the subtitle suggests....I recommend it to anyone who has an interest in learning something new about statistical inference. There is something here for everyone." The American Statistician, May 2005

"As the title of the book suggests, ‘All of Statistics’ covers a wide range of statistical topics. … The number of topics covered in this book is vast … . The greatest strength of this book is as a first point of reference for a wide range of statistical methods. … I would recommend this book as a useful and interesting introduction to a large number of statistical topics for non-statisticians and also as a useful reference book for practicing statisticians." (Matthew J. Langdon, Journal of Applied Statistics, Vol. 32 (1), January, 2005)

"This book was written specifically to give students a quick but sound understanding of modern statistics, and its coverage is very wide. … The book is extremely well done … ." (N. R. Draper, Short Book Reviews, Vol. 24 (2), 2004)

"This is most definitely a book about mathematical statistics. It is full of theorems and proofs … . Presuming no previous background in statistics … this certainly would be my choice of textbook if I was required to learn mathematical statistics again for a couple of semesters." (Eric R. Ziegel, Technometrics, Vol. 46 (3), August, 2004)

"The author points out that this book is for those who wish to learn probability and statistics quickly … . this book will serve as a guideline for instructors as to what should constitute a basic education in modern statistics. It introduces many modern topics … . Adequate references are provided at the end of each chapter which the instructor will be able to use profitably … ." (Arup Bose, Sankhya, Vol. 66 (3), 2004)

"The amount of material that is covered in this book is impressive. … the explanations are generally clear and the wide range of techniques that are discussed makes it possible to include a diverse set of examples … . The worked examples are complemented with numerous theoretical and practical exercises … . is a very useful overview of many areas of modern statistics and as such will be very useful to readers who require such a survey. Library copies would also see plenty of use." (Stuart Barber, Journal of the Royal Statistical Society, Series A – Statistics in Society, Vol. 168 (1), 2005)

• Department of Statistics, Carnegie Mellon University, Pittsburgh, USA

  Larry Wasserman

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.