WINNER OF THE 2005 DEGROOT PRIZE! 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.
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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.
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Probability is a mathematical language for quantifying uncertainty. Read the first page
Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
Probability is a mathematical language for quantifying uncertainty. Read the first page
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Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
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Most Helpful Customer Reviews
10 of 10 people found the following review helpful
By B. C. Meijer
Format:Hardcover
Looking at the table of contents one would think that this is the one book one would need to become a statistician. The subject is built up from elementary probability theory, and the book goes all the way to Monte Carlo Markov Chain methods.
The author manages to cover a lot of statistical theory in 442 pages. He does this by giving most theorems without proof, but often with a rationale for having the theorem. One can see where the journey is going, but the reader will have to take many of the actual steps for him- or herself. In the exercises the reader is asked to supply some of the missing proofs, and it is often a good idea to try and prove a theorem even if one is not explicitly asked to.
Its conciseness makes the book useful as a reference manual for people who already know statistics. Learning statistics from it requires a fairly solid mathematical background and a lot of effort.
The author manages to cover a lot of statistical theory in 442 pages. He does this by giving most theorems without proof, but often with a rationale for having the theorem. One can see where the journey is going, but the reader will have to take many of the actual steps for him- or herself. In the exercises the reader is asked to supply some of the missing proofs, and it is often a good idea to try and prove a theorem even if one is not explicitly asked to.
Its conciseness makes the book useful as a reference manual for people who already know statistics. Learning statistics from it requires a fairly solid mathematical background and a lot of effort.
Most Helpful Customer Reviews on Amazon.com (beta)
Amazon.com:
4.6 out of 5 stars
18 reviews
59 of 60 people found the following review helpful
5.0 out of 5 stars
all of statistics in just this little book?
6 April 2008
By Michael R. Chernick
- Published on Amazon.com
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.
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.
32 of 32 people found the following review helpful
5.0 out of 5 stars
A very accessable survey of many modern statistical techniques
18 Oct 2005
By a reader
- Published on Amazon.com
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.
30 of 30 people found the following review helpful
4.0 out of 5 stars
Excellent at times, but only a summary or introduction: far from thorough
2 Oct 2007
By Alexander C. Zorach
- Published on Amazon.com
Format:Hardcover|Amazon 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. In my opinion most students would benefit from a text that offers more explanation of the meaning and driving ideas behind theory. However, I like the way this book gets to the main points quickly and omits confusing and tedious details and irrelevant tangents. This book may be good for students who are briefly studying statistics and will never take a future course. This book is useful as a very basic reference, but I think its best use is for self-study--advanced students will find it one of the quickest and best ways to get an overview of most of the fundamental topics in theoretical statistics.
Honestly, I think Wasserman is an outstanding writer, and part of me wishes he would expand this book to the scale of something like Casella and Berger's "Statistical Inference", covering more material and adding more discussion of certain topics, but retaining the style of being to-the-point and omitting tedious details. I think this is one of the best books of its type out there but I refrain from giving 5 stars because I think Statistics is one area where most of the 5 star books have not yet been written.
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. In my opinion most students would benefit from a text that offers more explanation of the meaning and driving ideas behind theory. However, I like the way this book gets to the main points quickly and omits confusing and tedious details and irrelevant tangents. This book may be good for students who are briefly studying statistics and will never take a future course. This book is useful as a very basic reference, but I think its best use is for self-study--advanced students will find it one of the quickest and best ways to get an overview of most of the fundamental topics in theoretical statistics.
Honestly, I think Wasserman is an outstanding writer, and part of me wishes he would expand this book to the scale of something like Casella and Berger's "Statistical Inference", covering more material and adding more discussion of certain topics, but retaining the style of being to-the-point and omitting tedious details. I think this is one of the best books of its type out there but I refrain from giving 5 stars because I think Statistics is one area where most of the 5 star books have not yet been written.
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4.6 out of 5 stars
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