- Series: Springer Texts in Statistics
- Hardcover: 538 pages
- Publisher: Springer; 2010 edition (September 17, 2010)
- Language: English
- ISBN-10: 0387938389
- ISBN-13: 978-0387938387
- Product Dimensions: 6.1 x 1.2 x 9.2 inches
- Shipping Weight: 2.1 pounds (View shipping rates and policies)
- Average Customer Review: 6 customer reviews
- Amazon Best Sellers Rank: #161,025 in Books (See Top 100 in Books)
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Theoretical Statistics: Topics for a Core Course (Springer Texts in Statistics) 2010th Edition
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From the reviews:
“The book is innovative in the presentation and in mashing the traditional material with modern topics. The presentation shows a great mastery of the subject. … recommended to someone who has a working knowledge of statistics and would like to learn more about the theory. … As a text for a course, the book is versatile. … The mathematical level is correct for a first year graduate course and may be appropriate at some universities for courses whose primary audience is seniors.” (Stephan Morgenthaler, Mathematical Reviews, Issue 2011 m)
“This volume provides an excellent course in the mathematical theory underlying statistical ideas and methods, for advanced … students. The amount of material covered is indicated by the fact that it evolved from a three-semester sequence of courses given by the author. Its suitability as a course text is materially aided by very extensive exercises, along with solutions to selected exercises. Anyone who works through this book will end up with a first class understanding of the mathematical ideas underlying modern statistical concepts and methods.” (David J. Hand, International Statistical Review, Vol. 80 (1), 2012)
“The book extensively covers classic and modern topics of theoretical statistics in a rigorous manner. … The book provides more than 400 exercise problems. … There are many books on statistical theory but very few have such great breadth and scope of materials as this book. … the book is well written and it is a great addition to the collection of books on statistical theory. … It will serve well both as a textbook and a reference book.” (Xianggui Qu, Technometrics, Vol. 53 (3), August, 2011)
From the Back Cover
Intended as the text for a sequence of advanced courses, this book covers major topics in theoretical statistics in a concise and rigorous fashion. The discussion assumes a background in advanced calculus, linear algebra, probability, and some analysis and topology. Measure theory is used, but the notation and basic results needed are presented in an initial chapter on probability, so prior knowledge of these topics is not essential. The presentation is designed to expose students to as many of the central ideas and topics in the discipline as possible, balancing various approaches to inference as well as exact, numerical, and large sample methods. Moving beyond more standard material, the book includes chapters introducing bootstrap methods, nonparametric regression, equivariant estimation, empirical Bayes, and sequential design and analysis. The book has a rich collection of exercises. Several of them illustrate how the theory developed in the book may be used in various applications. Solutions to many of the exercises are included in an appendix. Robert Keener is Professor of Statistics at the University of Michigan and a fellow of the Institute of Mathematical Statistics.
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Top customer reviews
If, unlike these 2-star reviews, you have the recommended prerequisite skills, and a bit of math maturity, you'll find the text rigorous, but not tedious; he gives enough detail to make things clear, but occasionally leaves out the details in steps that a grad student should be able to fill.
The text is written in a fairly conversational manner, which is my favorite tone for a math text.. I'm not particularly fond of encyclopedic texts. So far, I've found his remarks to be tastefully plentiful, insightful, and informative.
Lastly,-- I may update this later-- I've found the large selection of problems to be of very high quality. All of the theoretical concepts in the first four chapters I have worked through are accompanied by simple, yet illustrative exercises(and examples) that have allowed me to develop a stronger sense for what I'm doing when working with the abstract. Naturally, there are exercises that vary in difficulty.. I try to work on a bit of everything. The best part about using this for self-study is that so many of the exercises have highly detailed solutions.
FYI: the first sixish chapters--and various later ones--cover the standard material in a first advanced sequence in statistics. The rest of the book seems to be a survey in some of the major areas of modern ststistics, which means a pretty picture of some of what's out there.
This is all I have for now.
EK-- a second year grad student in math
1) Provides a list of the topics that one should cover in a first year statistics PhD course.
2) Provides solutions to some exercises, which is useful for study.
1) As others have mentioned, it seems that Professor Keener is simply not able to put himself into the mind of an individual whom does not have a lucid understanding of this material. My professor loves this book even though all of his students constantly bemoan it. So it's probably great if you're already an expert in the material.
2) There is not intuition or reasoning or exposition. The text babbles for pages and pages of dense material without any insight into why the material matters. Often, Keener will introduce a key theorem or result after he proves it. This creates a jarring narrative that's impossible to follow.
3) Chapter 7 is the biggest pile of garbage I've ever read in a textbook. I have no idea how anyone thought it was acceptable to publish this chapter. 2 pages of moving symbols around for no explained reason followed by several pages of hand-waived examples and then the strangest justification of the estimator risk as a metric I've ever seen.
You'd be better off using a botany book to learn statistics.