All of Statistics

Larry Wasserman is a statistics professor at Carnegie Mellon University. He is also a member of the School of Computer Science's Center for Automated Learning and Discovery. Nonparametric inference, asymptotic theory, causality, and applications to astrophysics, bioinformatics, and genetics are among his research interests. He received the Committee of Presidents of Statistical Societies Presidents' Award in 1999 and the Centre de recherches mathematiques de Montréal-Statistical Society of Canada Prize in Statistics in 2002. He is a member of the editorial boards of The Journal of the American Statistical Association and The Annals of Statistics. He is an American Statistical Association and Institute of Mathematical Statistics fellow.

The title "All of Statistics" is an exaggeration if taken literally. However, the title is appropriate in spirit, as the book covers a much broader range of topics than a typical introductory book on mathematical statistics. This book is intended for people who want to quickly learn probability and statistics. It is appropriate for graduate or advanced undergraduate students studying computer science, mathematics, statistics, and other related fields.

Modern topics covered in the book include non-parametric curve estimation, bootstrapping, and classification, which are typically reserved for advanced courses. The reader is assumed to be familiar with calculus and a little linear algebra. There is no prior knowledge of probability or statistics required. Data collection and analysis are central to statistics, data mining, and machine learning.

Author: Larry Wasserman

Link to buy: https://www.amazon.com/All-Statistics-Statistical-Inference-Springer/dp/0387402721

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