PDF Ebook All of Statistics: A Concise Course in Statistical Inference (Springer Texts in Statistics)
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All of Statistics: A Concise Course in Statistical Inference (Springer Texts in Statistics)
PDF Ebook All of Statistics: A Concise Course in Statistical Inference (Springer Texts in Statistics)
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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)
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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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Product details
Series: Springer Texts in Statistics
Hardcover: 442 pages
Publisher: Springer; 1st Corrected ed. 20 edition (September 17, 2004)
Language: English
ISBN-10: 0387402721
ISBN-13: 978-0387402727
Product Dimensions:
6.1 x 1 x 9.2 inches
Shipping Weight: 2 pounds (View shipping rates and policies)
Average Customer Review:
3.6 out of 5 stars
41 customer reviews
Amazon Best Sellers Rank:
#59,475 in Books (See Top 100 in Books)
I've spent a lot of time with this book, reading through and working through each example. I've also found problem sets and solutions on the course website for CMU's intermediate stats course that is taught by the author and uses this text (36-705 Intermediate Statistics). Despite what some reviewers say about this book being too dry or lacking background intuition, I still think this is a good book to have if you wish to work through topics in probability all the way up to statistical inference (my goal is to understand this stuff well enough to grok the theoretical underpinnings of machine learning). My advice for getting the most out of this book is to take it very slowly and to work your way through every example. To give you an idea of the pace: I've spent about 3 months part time working through the first 4 chapters. I also recommend cross referencing material when the examples provided are insufficient to understand the material. I've found that they are sufficient about 60-70% of the time. That is ok with me, as I don't have a hard time googling to find supporting examples or materials. At the beginning I took it particularly slow. The idea of random variables was hard to wrap my head around. It's ok though, there are a ton of resources online taking different approaches to explaining the concept. And once it clicks, it's great to come back to the concise theorems of probability laid out in chapter one and continue on. If the book took the time to explain the intuition behind every concept, it would be 2000 pages long.So this book isn't magic. You won't be able to breeze through it and understand "all of statistics" in a few weeks. But it provides a comprehensive roadmap into key topics, theorems and examples—the best I've found anywhere—and when the book is lacking in explanation or examples, there are easily googleable terms to find more.In case anyone finds it helpful, I've collected quite a few resources on studying probability and statistics here: (...)
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.
I find it hard to rate the quality of the book. I am from a non-mathematical background (I got no further than calculus in college), and I've been working for three years now on building math skills, especially statistical analysis and inference. I asked a fellow employee (whom I thought I could trust) for a recommendation on a good book for someone with rusty math skills who is trying to learn statistics. This was his recommendation.This is NOT the book for that purpose. I realized on my first perusal of the book that he was being snide and sarcastic, as I subsequently learned was his custom. This book is a reference, full of complex mathematical notation, that is excellent (so far as I can determine) for reviewing concepts you have already learned and mastered. It is the worst possible choice for someone who is just starting out on learning statistics.I can now, finally, begin to dip into this book at least in places, and follow the material. So I'm glad, in the end, that I got it. It will eventually prove useful to me.
This is the first book I pull down from the shelf when I need a refresher or I am working through some concept and feel like I am missing some foundation somewhere. Also have used it as supplemental text in self-instruction in a wide variety of areas. The explanations are practical, well-written and the notation supports the explanations (instead of the other way around). Lots of concepts that I have wrestled with in other texts finally clicked for me after reading the write-up in this book. Can't recommend it highly enough.
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. It wasn't enough of a problem for me to want to return the book, but annoying nonetheless.
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