All of Statistics: A Concise Course in Statistical Inference

✍ Scribed by Larry Wasserman (auth.)

• Publisher: Springer-Verlag New York
• Year: 2004
• Tongue: English
• Leaves: 446
• Series: Springer Texts in Statistics
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages i-xix
Front Matter....Pages 1-1
Probability....Pages 3-17
Random Variables....Pages 19-46
Expectation....Pages 48-61
Inequalities....Pages 63-69
Convergence of Random Variables....Pages 71-84
Front Matter....Pages 85-85
Models, Statistical Inference and Learning....Pages 87-96
Estimating the CDF and Statistical Functionals....Pages 97-105
The Bootstrap....Pages 107-118
Parametric Inference....Pages 119-148
Hypothesis Testing and p-values....Pages 149-173
Bayesian Inference....Pages 175-192
Statistical Decision Theory....Pages 193-205
Front Matter....Pages 207-207
Linear and Logistic Regression....Pages 209-229
Multivariate Models....Pages 231-238
Inference About Independence....Pages 239-249
Causal Inference....Pages 251-262
Directed Graphs and Conditional Independence....Pages 263-279
Undirected Graphs....Pages 281-289
Log-Linear Models....Pages 291-301
Nonparametric Curve Estimation....Pages 303-326
Front Matter....Pages 207-207
Smoothing Using Orthogonal Functions....Pages 327-348
Classification....Pages 349-379
Probability Redux: Stochastic Processes....Pages 381-401
Simulation Methods....Pages 403-433
Back Matter....Pages 434-444

✦ Subjects

Statistical Theory and Methods; Probability and Statistics in Computer Science; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences