1999 NonparametricStatisticalMethods

• (Hollander & Wolfe, 1999) ⇒ Myles Hollander, Douglas A. Wolfe. (1999). “Nonparametric Statistical Methods, 2nd Edition.” In: Wiley. ISBN:0471190454

Subject Areas: Nonparametric Estimation Algorithm, Categorial Data Statistical Analysis, Longitudinal Statistical Analysis, Multivariate Statistical Analysis.

Notes

• Glossary (Appendix C) is online at ftp://ftp.wiley.com/public/sci_tech_med/nonparametric_stats/appendc.tex
• Homepage at Wiley Inc. http://ca.wiley.com/WileyCDA/WileyTitle/productCd-0471190454,descCd-relatedWebsites.html

Cited By

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Quotes

Book Overview

The importance of nonparametric methods in modern statistics has grown dramatically since their inception in the mid-1930s. Requiring few or no assumptions about the populations from which data are obtained, they have emerged as the preferred methodology among statisticians and researchers performing data analysis. Today, these highly efficient techniques are being applied to an ever-widening variety of experimental designs in the social, behavioral, biological, and physical sciences.

This long-awaited Second Edition of Myles Hollander and Douglas A. Wolfe's successful Nonparametric Statistical Methods meets the needs of a new generation of users, with completely up-to-date coverage of this important statistical area. Like its highly acclaimed predecessor, the revised edition, along with its companion ftp site, aims to equip students with the conceptual and technical skills necessary to select and apply the appropriate procedures for a given situation. An extensive array of examples drawn from actual experiments illustrates clearly how to use nonparametric approaches to handle one- or two-sample location and dispersion problems, dichotomous data, and one-way and two-way layout problems. Rewritten and updated, this Second Edition now includes new or expanded coverage of:

• Nonparametric regression methods.
• The bootstrap.
• Contingency tables and the odds ratio.
• Life distributions and survival analysis.
• Nonparametric methods for experimental designs.
• More procedures, real-world data sets, and problems.
• Illustrated examples using Minitab and StatXact.

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1.1 Advantages of Nonparametric Methods

Roughly speaking, a nonparametric procedure is a statistical procedure that has certain desirable properties that hold under relatively mild assumptions regarding the underlying populations from which the data are obtained. That rapid and continuous development on nonparametric statistical procedures over that past six decades is due to the following advantages enjoyed by nonparametric techniques:

• 1. Nonparametric methods require few assumptions about the underlying populations from which the data are obtained. In particular, [[Nonparametric Statistical Modeling Algorithm|nonparametric procedures== forgo the traditional assumption that the underlying populations are normal.
• 2. Nonparametric procedures enable the user to obtain exact P-values for tests, exact coverage probabilities for confidence intervals, each experientwise error rates for multiple comparison procedures, and exact coverage probabilities for confidence bands without relying on assumptions that the underlying populations are normal.
• 3. …

1.2 The Distribution-Free Property

The term nonparametric, introduced in Section 1.1, is imprecise. The related term distribution-free has precise meaning.

…

Appendix. C - Glossary

ftp://ftp.wiley.com/public/sci_tech_med/nonparametric_stats/appendc.tex

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References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
1999 NonparametricStatisticalMethods	Myles Hollander Douglas A. Wolfe			Nonparametric Statistical Methods, 2nd Edition			http://books.google.com/books?id=RJAQAQAAIAAJ