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Computer Intensive Statistical Methods: Validation, Model Selection, and Bootstrap, by J. S. Urban. Hjorth
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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.
- Sales Rank: #426159 in Books
- Published on: 1993-11-01
- Original language: English
- Number of items: 1
- Dimensions: 8.75" h x 5.50" w x .75" l, 1.11 pounds
- Binding: Hardcover
- 272 pages
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
Most helpful customer reviews
26 of 26 people found the following review helpful.
model selection and computer-intensive/resampling approaches to statistical modeling and inference
By Michael R. Chernick
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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