Customer Reviews

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

5 star:		(12)
4 star:		(3)
3 star:		(0)
2 star:		(1)
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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.

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.

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.

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).

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.

The text book is small but the content is very concise as stated in the title. The good point is that it does not cover everything which may make the book hard to read and hard to follow. Instead, it just introduce the important and fundamental concepts necessary for learning statistical inference. It's good for reference purpose.

I'm taking a first year graduate course in statistical inference at UC Berkeley, for students who lack the presumed advanced undergraduate prep in statistics. I ordered the book in advance and used Part I to brush up on probability (my only prior exposure to probability was auxiliary to quantum mechanics, and during actuarial exam prep, which is at a slightly lower level as it doesn't involve asymptotic theory). Part I plus chapter 6 unified a lot of concepts, and provided me with a solid, big-picture view of probability and statistics. I come from a pure math background, so I sometimes find Wasserman's liberal use of notation a little dizzying. I also wish he would've added significantly more remarks in the appendices about the measure-theoretic underpinnings of the subject. For example he could have merely OUTLINED some proofs (of theorems that are otherwise taken for granted) without compromising the application-oriented tone of the book. Having said all that, this book still seems like a good starting point for those who are interested in USING statistics e.g. actuaries or research scientists, (not for those who plan on doing research in theoretical statistics).

I used this book as part of an introductory graduate level mathematical statistics course at UC Berkeley. It's a rather interesting idea - this one book aims to provide the ingredients necessary to understand probability theory, statistical theory, and many commonly used techniques from applied statistics.

The book succeeds admirably in its purpose. You wouldn't want it to be the only statistics book in your collection (though if you *had* to pick just one, it wouldn't be a bad choice), but I frequently use it for two reasons:

1) to refresh my memory of the basic ideas in some subfield that I haven't thought about in awhile.
2) to orient myself to an area I've never learned anything about before diving into a source with more detail.

For both these purposes, this book provides just the right amount of depth, taking you through the most basic results and most important concepts without getting bogged down in minor points. Reading this book is like standing on top of a mountain with a 360 degree view - the big picture is brought into relief partly because some of the details are left obscure.

One note on a use to which this book should not be put: I wouldn't use this book as an introduction for those interested in self-study. If you have a teacher to flesh things out for you, it's great, but you may come out with a somewhat skeletal understanding if you rely strictly on this book.

This is a good overview of basic topics in statistics. The book isn't long, but the topics are chosen well. For instance, the third chapter on statistical inference is on bootstrapping. Bayesian statistics, causal inference, graphs, non-parametric statistics, and MCMC methods are all worthy subjects which Wasserman covers but are often omitted in other introductory texts.

I like this book much better than Casella and Berger---it is clearer and the subjects are chosen better. But like Casella and Berger, it is meant for someone already very familiar with math (don't buy this book if you've only taken 3 math classes).

Excellent book but particularly if you're already familiar with basic stats 101 and want a high level understanding of where to go from there. There are lots of references after each chapter to lookup for greater depth. The author builds intuition rapidly and the book is well organized for easy skimming and re-read points of interest. This will be near my desk for years. Very practical!