All of Statistics

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.

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)

• 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.