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All of Statistics: A Concise Course in Statistical Inference (Springer Texts in Statistics) [Paperback]

Larry Wasserman
4.5 out of 5 stars  See all reviews (22 customer reviews)

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Book Description

December 2, 2010 1441923225 978-1441923226
Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like nonparametric curve estimation, bootstrapping, and clas­ sification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analyzing data. For some time, statistics research was con­ ducted in statistics departments while data mining and machine learning re­ search was conducted in computer science departments. Statisticians thought that computer scientists were reinventing the wheel. Computer scientists thought that statistical theory didn't apply to their problems. Things are changing. Statisticians now recognize that computer scientists are making novel contributions while computer scientists now recognize the generality of statistical theory and methodology. Clever data mining algo­ rithms are more scalable than statisticians ever thought possible. Formal sta­ tistical theory is more pervasive than computer scientists had realized.

Frequently Bought Together

All of Statistics: A Concise Course in Statistical Inference (Springer Texts in Statistics) + Pattern Recognition and Machine Learning (Information Science and Statistics) + The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
Price for all three: $251.65

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Editorial Reviews

Review

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)

From the Back Cover

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.


Product Details

  • Series: Springer Texts in Statistics
  • Paperback: 442 pages
  • Publisher: Springer (December 2, 2010)
  • Language: English
  • ISBN-10: 1441923225
  • ISBN-13: 978-1441923226
  • Product Dimensions: 0.9 x 6.1 x 9.1 inches
  • Shipping Weight: 1.6 pounds (View shipping rates and policies)
  • Average Customer Review: 4.5 out of 5 stars  See all reviews (22 customer reviews)
  • Amazon Best Sellers Rank: #347,837 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews
61 of 62 people found the following review helpful
5.0 out of 5 stars all of statistics in just this little book? April 5, 2008
Format:Hardcover
Wasserman wrote a book titled "All of Nonparametrics." You can see my review of that on amazon. That also was a concise treatment of the subject in a book that covered more topics than say Conover's fine book but yet in less pages. The trick was to give the basics,provide references and offer the reader a broad perspective on the topic without going through the nitty gritty details. I was impressed at the way the author achieved his goal and addressed topics like nonparametric regression and wavelets that are not normally covered in a first course in nonparametrics.

Covering all of statistics in just slightly more pages seems at first an insane notion. The approach is the same as in the other book but with so much more to cover the treatment is a little less detailed and a little more concise. The reader needs to realize that the title is intentionally misleading. In both cases it is not Wasserman's intention to really cover every aspect of the subject at hand. Rather it is a carefully chosen selection of essential topics written in a concise but still very clear and lucid way. I think a more appropriate title would have been "All You Really Need to Know About Statistics That You Were Afraid to Ask." I think the author might consider such a change of title in a revised edition. I would have the same typr of title change for the Nonparametrics book as well. These books are different from the standard fare for introductory texts. But if you want a overview of the subject where the author points you in the right direction for dotting the i's and crossing the t's, this is the right book for you. For practitioners who are not statisticians this usually what they are looking for. For statisticians it is a useful reference source to go along with other texts on statistical inference.
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34 of 35 people found the following review helpful
Format:Hardcover
This book provides a survey of many modern statistical techniques such as bootstrapping and modern classification methods, as well as presenting the fundamentals of inferential theory. The book appears to be aimed at an audience conversant in mathematics, but more interested in a general overview of methods than rigor and limit theorems. As such, it presents brief and readable introductions to topics such as support vector machines, kernel estimation and Markov Chain Monte Carlo Methods that usually only appear in more specialized literature. On the whole I found it a very useful and readable text. A minor criticism is that there are a fair number of typographical errors, especially in equations in the later chapters; presumably this will be fixed in subsequent editions.
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37 of 39 people found the following review helpful
Format:Hardcover|Verified Purchase
This book is essentially a summary of the major theoretical topics in statistics, at an introductory level. The focus is on theory, not on data analysis or modeling, but there are more connections to data analysis and modeling than is typical among books on the same topics. The main flaw in this book is not that it does anything poorly, but rather, that it omits a lot.

The book is very balanced in its coverage of different topics, its discussion of the frequentist vs. Bayesian paradigm, etc. It mentions parametric and nonparametric inference, including hypothesis testing, point estimation, Bayesian inference, decision theory, regression, and even two different approaches to causal inference. The book also paints a fairly whole picture of how the different topics relate to each other and fit into a unified theoretical framework. Another huge strength of this book is that it always omits unnecessary technical details, including only streamlined discussions highlighting essential points.

The main weakness of this book is that certain topics are only brushed upon and not adequately explained. The first two chapters are deep enough for students to get a more or less complete understanding of the important ideas (assuming they do the exercises). But, for example, the 4th chapter covering inequalities is simply a collection of equations and formulas: the text explains how to use them, but not where they come from or what their intuitive interpretation is. This problem arises throughout the book but it is most evident in chapter 4. I want to remark, however, that this problem is widespread in statistics textbooks, and this book is still less lacking in this respect than is common among typical texts.

I'm not sure this book makes the best textbook.
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30 of 31 people found the following review helpful
5.0 out of 5 stars well written December 13, 2004
By Jump
Format:Hardcover
This is a very well written book. Does a good job of reviewing the fundamental concepts and also hitting on advanced topics, has well chosen examples and problems, and is clearly organized and written.

This is a good choice for a computer scientist who is getting into statistics for the first time or needs a refresher. It would also be a very good choice for self study.

The level of this book is approximately that of "Pattern Classification" (also a good book) or the slightly more advanced "The Elements of Statistical Learning" (which I would not recommend).
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21 of 22 people found the following review helpful
5.0 out of 5 stars Great for a quick summary of the basics March 1, 2006
Format:Hardcover|Verified Purchase
I have not read every section, but have found that it is a nice place to get a quick summary of the main results in some of the more outlying regions of statistics. I would not use it for a course because of its brevity, but I have recommended it to my class of future statisticians as a nice capsule reference book.
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Most Recent Customer Reviews
2.0 out of 5 stars Great book - but very poor print quality
A brief note about the book: I quite like it. I'm planning to teach from it. It covers a lot of ground quickly by skipping a lot of proofs, but the presentation language is... Read more
Published 27 days ago by David S. Rosenberg
4.0 out of 5 stars Good book with binding problem
I'm not finished with the book but find it very readable and helpful. I was a little disappointed to receive the book and notice that there was a problem with its binding. Read more
Published 1 month ago by Tach
5.0 out of 5 stars Much better coverage of modern methods than most introductory texts
Most books at the level of this one focus on classical procedures that were in common use decades ago. Read more
Published 2 months ago by M. D. HEALY
5.0 out of 5 stars Overview of modern statistics with introduction to machine learning
This book gives an overview of classical statistics, with an introduction to more modern methods of robust estimation and machine learning. Read more
Published 7 months ago by mikepol
4.0 out of 5 stars Perfect introduction to statistics -- needs screening for errors
The book presents an ultimate introduction to statistics with references to the literature for the interested reader. Read more
Published 15 months ago by R. Phlypo
5.0 out of 5 stars Good book but very concise
Book arrived quickly and in perfect condition. In terms of the material wasserman is very concise so this book needs another book or lots of googling along with it to com
Published 21 months ago by Marcus
4.0 out of 5 stars Good encyclopedic reference
This book is valuable for practitioners as well as students of mathematical statistics.
The material is very perspicuously presented. Read more
Published on September 6, 2011 by Hossain Pezeshki
5.0 out of 5 stars A wide-ranging reference
I used this book as part of an introductory graduate level mathematical statistics course at UC Berkeley. Read more
Published on July 18, 2011 by M. D. Edge
5.0 out of 5 stars Excellent book
This book is an excellent book for the people who want to know statistics in a short time. It covers many important topics in statistics and is very useful for the people in... Read more
Published on July 7, 2011 by Liang Sun
5.0 out of 5 stars Excellent reference
Good book to use as a reference, not a great learning book but good if you have a base knowledge or need to come back and get clarification.
Published on May 15, 2011 by J. Richardson
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