How to use computer-intensive methods to assess the significance of a statistic in an hypothesis test--for both statisticians and nonstatisticians alike. The significance of almost any test can be assessed using one of the methods presented here, for the techniques given are very general (e.g. virtually every nonparametric statistical test is a special case of one of the methods covered). Programs presented are brief, easy to read, require minimal programming, and can be run on most PC's. They also serve as templates adaptable to a wide range of applications. Includes numerous illustrations of how to apply computer-intensive methods.
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How to use computer-intensive methods to assess the significance of a statistic in an hypothesis test--for both statisticians and nonstatisticians alike. The significance of almost any test can be assessed using one of the methods presented here, for the techniques given are very general (e.g. virtually every nonparametric statistical test is a special case of one of the methods covered). Programs presented are brief, easy to read, require minimal programming, and can be run on most PC's. They also serve as templates adaptable to a wide range of applications. Includes numerous illustrations of how to apply computer-intensive methods.
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31 of 31 people found the following review helpful:
3.0 out of 5 stars
not the best for bootstrap tests,
By
This review is from: Computer-Intensive Methods for Testing Hypotheses: An Introduction (Paperback)
The text is only 92 pages. There are 102 pages of basic programs, 25 pages of statistical tables and 4 pages of references. Some of the references are interesting. The advantage of this book is that it is mostly written on a non-technical level, although some treatment of the bootstrap is a little technical and awkwardly written. It also is nice that it introduces Monte Carlo before the bootstrap so that the bootstrap (actually the Monte Carlo approximation to the bootstrap) can be seen as a special case of Monte Carlo testing of hypotheses. Among the disadvantages are (1) it deals solely with hypothesis testing, many readers may be interested in other applications such as subset selection, bias correction and confidence interval estimation, (2) written in 1989 it misses the many advances from 1989 to 2000 and even avoids some of the more complex but possibly better approaches that were available in 1989, and (3) it lacks depth. Readers interested in more depth without loss of good exposition can turn to Manly (1997) Randomization, Bootstrap and Monte Carlo Methods in Biology. Well written books dealing strictly with permutation methods of hypothesis testing are Good (2000) Permutation Tests A Practical Guide to Resampling Methods for Tesing Hypotheses 2nd Edition and Edgington (1995), Randomization Tests 3rd Edition. Excellent books devoted to bootstrap are Efron and Tibshirani (1993) An Introduction to the Bootstrap, Davison and Hinkley (1997) Bootstrap Methods and their Applications and Chernick (1999) Bootstrap Methods: A Practitioner's Guide.
4 of 4 people found the following review helpful:
5.0 out of 5 stars
Excellent introduction to computer intensive methods,
By SCHIELD@AUGSBURG.EDU (Minneapolis, Minnesota USA) - See all my reviews
This review is from: Computer-Intensive Methods for Testing Hypotheses: An Introduction (Paperback)
This is a great foundation book. Separate chapters on approximate randomization, Monte Carlo sampling and bootstrap resampling. Good discussion of strengths and weaknesses of each. Very readable, well paced, good appendices with additional technical detail.
4.0 out of 5 stars
Too Old,
By
Amazon Verified Purchase(What's this?)
This review is from: Computer-Intensive Methods for Testing Hypotheses: An Introduction (Paperback)
I need to find a way to do this without using Pascal, Fortran or Basic. Surely SPSS, SAS or Excel support approximate randomization, but this book was written too long ago to mention them. It's a great book for the basic theory, but don't count on it to help you actually do the work. Dr. Noreen: please write an update!
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The next few years are likely to be an exciting period for those involved in testing hypotheses. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
nontransfer students, shift method bootstrap, unshuffled data, distribution shift method, relative slave holdings, approximate randomization test, conventional parametric tests, joint bootstrap, test execution times, bootstrap resampling test, randomization test null hypothesis, conventional parametric methods, bootstrap sampling distribution, bootstrap randomization test, null hypothesis sampling distribution, slave holding counties, auxiliary randomization, null hypothesis population, medium counties, simulated random samples, global variable definitions, derived data sets, underlying sampling distribution, variable strata, program appendices Key Phrases - Capitalized Phrases (CAPs): (learn more)
Monte Carlo, Results Actual, Numerical Recipes Software, Polish Premium, Russian Budget, Polish Budget, Russian Premium, University of Washington, Consumers Union of United States, Mount Vernon, Results Sample, Shuffling There New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
The next few years are likely to be an exciting period for those involved in testing hypotheses. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
nontransfer students, shift method bootstrap, unshuffled data, distribution shift method, relative slave holdings, approximate randomization test, conventional parametric tests, joint bootstrap, test execution times, bootstrap resampling test, randomization test null hypothesis, conventional parametric methods, bootstrap sampling distribution, bootstrap randomization test, null hypothesis sampling distribution, slave holding counties, auxiliary randomization, null hypothesis population, medium counties, simulated random samples, global variable definitions, derived data sets, underlying sampling distribution, variable strata, program appendices Key Phrases - Capitalized Phrases (CAPs): (learn more)
Monte Carlo, Results Actual, Numerical Recipes Software, Polish Premium, Russian Budget, Polish Budget, Russian Premium, University of Washington, Consumers Union of United States, Mount Vernon, Results Sample, Shuffling There 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 8
books:
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7
books
cite this book:
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- Numerical Recipes: The Art of Scientific Computing by William H. Press on 12 pages
- Randomization Tests (Statistics: A Series of Textbooks and Monographs) by Eugene Edgington on page 13, Back Matter (1), and Back Matter (2)
- The Design of Experiments by Ronald A. Fisher on page 32, and Back Matter
- Nonparametric Statistical Methods, 2nd Edition by Myles Hollander in Back Matter
- Mathematical Statistics by Steven F. Arnold in Back Matter
See all 8 books this book cites
- Amniote Origins: Completing the Transition to Land by Stuart Sumida on page 488
- Analysis of Pretest-Posttest Designs by Peter L. Bonate in Back Matter
- Modeling Biological Systems:: Principles and Applications by James W. Haefner in Back Matter
- The Bootstrap and Edgeworth Expansion (Springer Series in Statistics) by Peter Hall in Back Matter
- Mechanical Reliability Improvement: Probability and Statistics for Experimental Testing (Mechanical Engineering) (v. 148) by Robert Little in Back Matter
See all 7 books citing this book
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