All of Statistics: A Concise Course in Statistical Inference (Springer Texts in Statistics) by Larry Wasserman (PDF)

Description

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.

User’s Reviews

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐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 book gives an overview of classical statistics, with an introduction to more modern methods of robust estimation and machine learning. I would say the contents are more focused on practical methods, but the author is always careful to state the necessary theorems from the underlying mathematical foundations of each method. Most of the theorems are stated without proof, although almost each chapter is followed by a short appendix giving some more technical details. Providing a proof for each theorem would take a lot of space and would detract from the applied aspects of this book. What I like is that each chapter has a nice list of references, so an interested reader could go on and explore each subject in more depth with all the mathematical details they need.The subjects covered is a compromise between the practical side of classical statistics and the modern methods of machine learning. They include convergence, the delta method, point estimation, hypothesis testing and confidence intervals, bootstrap, regression, non-parametric estimation, orthogonal functions, classification, graphical models, and monte carlo for integral evaluation. There is some bayesian estimation, but mostly the book follows a frequentist approach.I think that this book would be useful only for someone already familiar with classical statistics. It could serve as a good modern reference on statistics and an overview of some methods from machine learning. I do not think that this book is a good source for first exposure to these ideas. Someone should first go through a standard statistics book, such as for example Casella & Berger or Bickel & Doksum. Then this book could server as a “crossover” from that classical material to the modern methods of machine learning. After that the reader can go on to explore machine learning literature on their own, using this book as a guide.There are a small number of typos throughout the book. They pick up in chapter 22 on classification, where there are some typos in important equations, for example equation 22.21 on Fisher discriminant and the formula for epsilon in theorem 22.17. But overall I had a very positive experience reading this book. It helped me review some stuff I already learned, showed some new applications, and introduced some topics which I look forward to exploring further.

⭐This is hands-down the best book on statistics I have ever come across. It doesn’t get bogged down in unnecessary mathematical detail, nor does it patronise the reader with trivial examples. Somehow the author manages to communicate concepts intuitively and efficiently, without seeming dry. If you are looking for a swift and clear way to learn statistics, this is the book for you.Disclaimer: this book is aimed at advanced undergraduates and beginning graduate students who are looking to learn some statistics for application in computer science. If you are a hard-core mathematician, you are likely to find it frustratingly non-rigorous. Likewise, if you are an untrained scientist, you may find the mathematical style alien. But if, like me, you are a theoretical physicist, the material is refreshingly light, the approach is pleasingly logical, and unnecessary calculations are left to the reader – a perfect balance for serious study.

⭐A concise and well balanced book amalgamating theory and applications. Great and clear examples for application of the theory and exercises to grow more in depth understanding.

⭐Comprehensive, a little too formal and terse perhaps but worth the money.

⭐I am satisfied

⭐Apenas he terminado la parte de probabilidad (alrededor de la pagina 80), y hasta ahora el contenido me ha parecido muy bueno; lo primero que note que me llamo la atención fue la cantidad de información que el libro contiene en apenas unas paginas, no da rodeos y en mi perspectiva explica las cosas de una manera muy clara, lo que si se tiene que tener en cuenta es que se necesita un conocimiento previo de matemáticas decente , en especial se necesita poder entender calculo, álgebra, formalismo matemático y algo de álgebra lineal.

Keywords

Free Download All of Statistics: A Concise Course in Statistical Inference (Springer Texts in Statistics) in PDF format
All of Statistics: A Concise Course in Statistical Inference (Springer Texts in Statistics) PDF Free Download
Download All of Statistics: A Concise Course in Statistical Inference (Springer Texts in Statistics) 2013 PDF Free
All of Statistics: A Concise Course in Statistical Inference (Springer Texts in Statistics) 2013 PDF Free Download
Download All of Statistics: A Concise Course in Statistical Inference (Springer Texts in Statistics) PDF
Free Download Ebook All of Statistics: A Concise Course in Statistical Inference (Springer Texts in Statistics)