All of Statistics: A Concise Course in Statistical Inference

Front Cover
Springer Science & Business Media, Sep 17, 2004 - Computers - 442 pages
8 Reviews

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.

  

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Review: All of Statistics: A Concise Course in Statistical Inference

User Review  - Juliusz Gonera - Goodreads

A bit harsh for an introduction, requires mathematical maturity. Great reference though. Read full review

Review: All of Statistics: A Concise Course in Statistical Inference

User Review  - Michiel - Goodreads

Very good reference on notions on probability, statistics and machine learning. Not ideal to learn the matter from scratch, but ideal to refresh and supplement your knowledge when you do a PhD. Read full review

Contents

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IV
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XI
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Copyright

Common terms and phrases

Popular passages

Page 427 - A high spatial resolution analysis of the MAXIMA-1 cosmic microwave background anisotropy data'.
Page 426 - GHOSAL, S., GHOSH, JK and VAN DER VAART, AW (2000). Convergence rates of posterior distributions. The Annals of Statistics 28 500-531.
Page 430 - Wright, S. (1934). The method of path coefficients. The Annals of Mathematical Statistics 5, 161-215.
Page 425 - DONOHO, DL and JOHNSTONE, IM (1994). Ideal spatial adaptation by wavelet shrinkage. Biometrika 81 425-455. DONOHO, DL and JOHNSTONE, IM (1995). Adapting to unknown smoothness via wavelet shrinkage.

References to this book

About the author (2004)

Larry Wasserman is Professor of Statistics at Carnegie Mellon University.

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