All of statistics [electronic resource] : a concise course in statistical inference

All of Statistics: A Concise Course in Statistical Inference
Author: Larry Wasserman
Published by Springer New York
ISBN: 978-1-4419-2322-6
DOI: 10.1007/978-0-387-21736-9

Table of Contents:

• Probability
• Random Variables
• Expectation
• Inequalities
• Convergence of Random Variables
• Models, Statistical Inference and Learning
• Estimating the CDF and Statistical Functionals
• The Bootstrap
• Parametric Inference
• Hypothesis Testing and p-values
• Bayesian Inference
• Statistical Decision Theory
• Linear and Logistic Regression
• Multivariate Models
• Inference About Independence
• Causal Inference
• Directed Graphs and Conditional Independence
• Undirected Graphs
• Log-Linear Models
• Nonparametric Curve Estimation

Includes bibliographical references (pages 423-430) and index
Print version record
Probability -- Random Variables -- Expectation -- Inequalities -- Convergence of Random Variables -- Models, Statistical Inference and Learning -- Estimating the CDF and Statistical Functionals -- The Bootstrap -- Parametric Inference -- Hypothesis Testing and p-values -- Bayesian Inference -- Statistical Decision Theory -- Linear and Logistic Regression -- Multivariate Models -- Inference about Independence -- Causal Inference -- Directed Graphs and Conditional Independence -- Undirected Graphs -- Loglinear Models -- Nonparametric Curve Estimation -- Smoothing Using Orthogonal Functions -- Classification -- Probability Redux: Stochastic Processes -- Simulation Methods
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 MontrealStatistical 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