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Mathematical Statistics with Resampling and R 1st Edition

4.3 out of 5 stars 9 customer reviews
ISBN-13: 978-1118029855
ISBN-10: 1118029852
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Editorial Reviews

Review

"Mathematical Statistics with Resampling and R is a great resource for intermediate and advanced statistics students who want to achieve an indepth understanding of resampling techniques backed by practical implementation." (Book Pleasures, 2012)

"It is highly recommended to someone with a good background in mathematics, probability, and basic statistics who wants to learn about the theory and about resampling and how it relates to traditional methods, and how to implement resamplinjg in R. The book is also a wonderful source of simulations to support the teaching of statistics." (Journal of Biopharmaceutical Statistics, 2011)

"It is less demanding mathematically, more applied in its emphasis, and more modern in content than the usual book, which makes it a good choice if you want a modern applied book at the level of Larsen and Marx (1986)."- George W. Cobb, Mount Holyoke College Department of Mathematics and Statsitics (Chilean Journal of Statistics, 1 April 2011)

From the Back Cover

This book bridges the latest software applications with the benefits of modern resampling techniques

Resampling helps students understand the meaning of sampling distributions, sampling variability, P-values, hypothesis tests, and confidence intervals. This groundbreaking book shows how to apply modern resampling techniques to mathematical statistics. Extensively class-tested to ensure an accessible presentation, Mathematical Statistics with Resampling and R utilizes the powerful and flexible computer language R to underscore the significance and benefits of modern resampling techniques.

The book begins by introducing permutation tests and bootstrap methods, motivating classical inference methods. Striking a balance between theory, computing, and applications, the authors explore additional topics such as:

  • Exploratory data analysis

  • Calculation of sampling distributions

  • The Central Limit Theorem

  • Monte Carlo sampling

  • Maximum likelihood estimation and properties of estimators

  • Confidence intervals and hypothesis tests

  • Regression

  • Bayesian methods

Throughout the book, case studies on diverse subjects such as flight delays, birth weights of babies, and telephone company repair times illustrate the relevance of the real-world applications of the discussed material. Key definitions and theorems of important probability distributions are collected at the end of the book, and a related website is also available, featuring additional material including data sets, R scripts, and helpful teaching hints.

Mathematical Statistics with Resampling and R is an excellent book for courses on mathematical statistics at the upper-undergraduate and graduate levels. It also serves as a valuable reference for applied statisticians working in the areas of business, economics, biostatistics, and public health who utilize resampling methods in their everyday work.

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Product Details

  • Hardcover: 440 pages
  • Publisher: Wiley; 1 edition (September 6, 2011)
  • Language: English
  • ISBN-10: 1118029852
  • ISBN-13: 978-1118029855
  • Product Dimensions: 6.5 x 1.1 x 9.6 inches
  • Shipping Weight: 1.7 pounds (View shipping rates and policies)
  • Average Customer Review: 4.3 out of 5 stars  See all reviews (9 customer reviews)
  • Amazon Best Sellers Rank: #562,626 in Books (See Top 100 in Books)

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Top Customer Reviews

Format: Hardcover
This is an undergraduate introductory text in mathematical statistics for sophomores or juniors who have had a first course in probability but no courses in statistical inference. The authors are experts in SPlus from having worked at Insightful Corporation and are also knowledgeable in R. Tim Hesterberg did his dissertation at Stanford on the bootstrap under Brad Efron. This covers all the traditional topics but has the special feature of introducing bootstrap and permutation methods treated equally with the classical inference methods. Resampling is introduced in a friendly way with good explanations and examples for illustration. Real examples are provided throughout to demonstrate all the methods in the text. The book is clear. The descriptions are accurate and many key references are included. Topics include exploratory data analysis, Hypothesis testing and confidence intervals (classical and resampling), sampling distributions, excellent introductions to bootstrap and permutation methods,regression (classical and bootstrap)and some special topics that include smoothed and parametric bootstrap and importance sampling.
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Format: Hardcover Verified Purchase
This was the assigned text for my statistical inference course last semester. It was confusing and poorly written. I do NOT recommend it. When I bought in on Amazon in January, I noticed the only people to recommend it knew the authors--first warning! There are MANY careless errors. In several chapters the notations change without notice, sometimes mid-proof! There are answers to some of the end-of-chapter exercises in the back, but they don't include explaination, and several of them are wrong. Figuring out the right answers was a nice class exercise.

The only good point is the exercises involving R, which are a nice way to illustrate the tests in the text. The companion website contains data sets, a brief R tutorial, and a partial list of errors. (You'll still need to pick up an R reference book). If this is your assigned text, good luck. Maybe it will be better in a later edition.
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Format: Hardcover
This is a very comprehensive book about statistics and R application. I benefited a lot from using this book to do different types of statistical analysis.
Not only does the book provide good and interesting real-life examples where we can apply statistics theories to examine them, but also offer good problems that help us to review various statistics topics.
I highly recommend this book especially if you consider to be a stats major.
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Format: Hardcover Verified Purchase
Honestly I've only had the book a few weeks but, it is very good. This review is based on running and reviewing the code for several chapters and skimming over most of the remaining book. It does well on conveying ideas through code, which is exactly what I wanted. The expression of ideas as simply as possible. However, for whatever reason it isn't as exhaustive as I would like. No sufficient statistics. Several times I've found I would've coded something more thoroughly or simply (in myopinion) but I'm sure that's part of the trade-off of writing a book. The authors say they had to omit some complexity to it's to make room for their code. Guess if they leave you wanting more they must've done their job though. Just leaving this review since I find them so helpful when I'm considering investing in new books. Hope you enjoy this book as much as I do.
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Format: Kindle Edition Verified Purchase
This is an excellent lightweight introduction to resampling methods, and in particular bootstrapping. If you're new to bootstrapping this is a great place to start. This book actually teaches the topic. It's not a collection of proofs.

If it falls short it is in that it does not go far enough. A second follow on volume is needed to teach more advanced applications of resampling and bootstrapping.
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