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Mathematical Statistics with Resampling and R, by Laura M. Chihara, Tim C. Hesterberg
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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.
- Sales Rank: #378340 in eBooks
- Published on: 2012-09-04
- Released on: 2012-09-04
- Format: Kindle eBook
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.
About the Author
LAURA CHIHARA, PhD, is Professor of Mathematics at Carleton College. She has extensive experience teaching mathematical statistics and applied regression analysis. She has supervised undergraduates working on statistics projects for local businesses and organizations such as Target Corporation and the Minnesota Pollution Control Agency. Dr. Chihara has experience with S+ and R from her work at Insightful Corporation (formerly MathSoft) and in statistical consulting.
TIM HESTERBERG, PhD, is Senior Ads Quality Statistician at Google. He was a senior research scientist for Insightful Corporation and led the development of S+Resample and other S+ and R software. Dr. Hesterberg has published numerous articles in the areas of bootstrap and related resampling techniques, Monte Carlo simulation methodology, modern regression, tectonic deformation estimation, and electric demand forecasting.
Most helpful customer reviews
13 of 18 people found the following review helpful.
math stats the resampling way
By Michael R. Chernick
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.
16 of 23 people found the following review helpful.
Careless errors
By trtc
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.
0 of 0 people found the following review helpful.
Good but with a lot of typos.
By Yang Xiaoli
It's a good book. It helps with gaining insight into the formulas and theorems etc. by getting around the math and showing with R scripts what is really going on. I do recommend this to anyone who might have gotten lost in a traditional formula based stat teaching. The only major complain I have is that typos are rampant everywhere in this book - just a little too much to be considered a serious book written with adequate professionalism. Make sure you download the latest errata, and be wary of many that aren't listed. This book deserve a major revision.
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