In engineering work and other practical situations, methods of a "non-stop" character are often needed. The computer intensive methods outlined in this book should show how to pass many obstacles that could not previously be overcome. Much emphasis in this book is placed on applications in science, economics, reliability, meteorology, medicine and transportation. In principle every area where data deserve statistical analyses there is a relevant application of these new methods. This book is aimed at classically educated statisticians as well as the younger generation. The key points of the book include: an introduction to the "bootstrap" method at elementary and advanced levels, examples of computer programmes and a review of the new methodology and its applications.
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Book Description
Editorial Reviews
Review
The book is written in an engaging, entertaining style, and is well worth a look...
-Short Book Reviews
The examples and case studies presented are interesting, and help to explain how cross-validation and bootstrapping work in practice.
-The Statistician
-Short Book Reviews
The examples and case studies presented are interesting, and help to explain how cross-validation and bootstrapping work in practice.
-The Statistician
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25 of 25 people found the following review helpful:
5.0 out of 5 stars
model selection and computer-intensive/resampling approaches to statistical modeling and inference,
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This review is from: Computer Intensive Statistical Methods: Validation, Model Selection, and Bootstrap (Hardcover)
This book provides a very clear treatment of model selection, the bootstrap and the computer-intensive philosophy. It places heavy emphasis on the bootstrap and cross validation. Other computer-intensive methods such as MCMC methods, multiple imputation and permutation methods are not treated. It represents development up to 1994 in a rapidly advancing area. There have been many additional theoretical and applied advances since then. It covers most of the topics of other competing books on bootstrap and resampling but is unique in its treatment of model selection and validation. It appears to require intermediate level knowledge of mathematics and freely uses matrix algebra. There are many good references and examples. The author uses FORTRAN to implement the methods and provides code. This may be a bit old fashioned. Current price of book is very high and these days there are many alternative texts to consider.
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Inside This Book
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First Sentence:
Some powerful and very fundamental new principles have been developed in statistics for model selection, estimation, and evaluation of uncertainty. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
cross model validation, model selection effects, bootstrapping regression models, model selection uncertainty, studentized interval, intensive philosophy, bias adjusted estimate, median estimation, bootstrap results, forward validation, double bootstrap, bootstrap analysis, bootstrap simulations, confidence interval methods, adjusted estimator, bootstrap distribution, bootstrap problem, computer intensive methods, censoring times, bootstrap replicates, parametric bootstrap, bootstrap sample, prediction formula, bootstrap confidence intervals, bootstrap method New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Some powerful and very fundamental new principles have been developed in statistics for model selection, estimation, and evaluation of uncertainty. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
cross model validation, model selection effects, bootstrapping regression models, model selection uncertainty, studentized interval, intensive philosophy, bias adjusted estimate, median estimation, bootstrap results, forward validation, double bootstrap, bootstrap analysis, bootstrap simulations, confidence interval methods, adjusted estimator, bootstrap distribution, bootstrap problem, computer intensive methods, censoring times, bootstrap replicates, parametric bootstrap, bootstrap sample, prediction formula, bootstrap confidence intervals, bootstrap method New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 22
books:
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2
books
cite this book:
- Statistics for Spatial Data (Wiley Series in Probability and Statistics) by Noel Cressie in Back Matter
- Time Series Analysis: Forecasting & Control (3rd Edition) by George Box in Back Matter
- Data Analysis (New Topics for Secondary School Mathematics) by Nc School of Sci and Math in Back Matter
- Time Series (International Library of Critical Writings in Econometrics) by Andrew C. Harvey in Back Matter
- Subset Selection in Regression, Second Editon by Alan Miller in Back Matter
See all 22 books this book cites
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