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140122 ||| eng |
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|a 9781475723465
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| 100 |
1 |
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|a Good, Phillip
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| 245 |
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|a Permutation Tests
|h Elektronische Ressource
|b A Practical Guide to Resampling Methods for Testing Hypotheses
|c by Phillip Good
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| 250 |
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|a 1st ed. 1994
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| 260 |
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|a New York, NY
|b Springer New York
|c 1994, 1994
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| 300 |
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|a X, 228 p. 9 illus
|b online resource
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| 505 |
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|a 1. A Wide Range of Applications -- 2. A Simple Test -- 3. Testing Hypotheses -- 4. Experimental Designs -- 5. Multivariate Analysis -- 6. Categorical Data -- 7. Dependence -- 8. Clustering in Time and Space -- 9. Coping with Disaster -- 10. Which Statistic? Solving the Insolvable -- 11. Which Test Should You Use? -- 12. Publishing Your Results -- 13. Increasing Computational Efficiency -- 14. Theory of Permutation Tests -- Bibliography Part 1: Randomization -- Bibliography Part 2: Supporting -- Bibliography Part 3: Computational Methods -- Bibliography Part 4: Seminal Articles
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| 653 |
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|a Mathematical and Computational Biology
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| 653 |
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|a Biostatistics
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| 653 |
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|a Biomathematics
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| 653 |
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|a Biometry
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| 041 |
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7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b SBA
|a Springer Book Archives -2004
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| 490 |
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|a Springer Series in Statistics
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| 028 |
5 |
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|a 10.1007/978-1-4757-2346-5
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| 856 |
4 |
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|u https://doi.org/10.1007/978-1-4757-2346-5?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
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|a 570,285
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| 520 |
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|a Permutation tests permit us to choose the test statistic best suited to the task at hand. This freedom of choice opens up a thousand practical applications, including many which are beyond the reach of conventional parametric sta tistics. Flexible, robust in the face of missing data and violations of assump tions, the permutation test is among the most powerful of statistical proce dures. Through sample size reduction, permutation tests can reduce the costs of experiments and surveys. This text on the application of permutation tests in biology, medicine, science, and engineering may be used as a step-by-step self-guiding reference manual by research workers and as an intermediate text for undergraduates and graduates in statistics and the applied sciences with a first course in statistics and probability under their belts. Research workers in the applied sciences are advised to read through Chapters 1 and 2 once quickly before proceeding to Chapters 3 through 8 which cover the principal applications they are likely to encounter in practice. Chapter 9 is a must for the practitioner, with advice for coping with real life emergencies such as missing or censored data, after-the-fact covariates, and outliers. Chapter 10 uses practical applications in archeology, biology, climatology, education and social science to show the research worker how to develop new permutation statistics to meet the needs of specific applications. The practitioner will find Chapter 10 a source of inspiration as well as a practical guide to the development of new and novel statistics
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