Skip Navigation

Biometrika 1975 62(2):425-432; doi:10.1093/biomet/62.2.425
© 1975 by Biometrika Trust
This Article
Right arrow Full Text (PDF)
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by LEE, E. T.
Right arrow Articles by GEHAN, E. A.
Right arrow Search for Related Content
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

A Monte Carlo study of the power of some two-sample tests

ELISA T. LEE, M. M. DESU and E. A. GEHAN

University of Texas, M.D. Anderson Hospital and Tumor Institute Houston
Department of Statistics, State University of New York Buffalo
University of Texas M.D. Anderson Hospital and Tumor Institute Houston

The powers of several two-sample tests are compared by simulation for small samples from exponential and Weibull distributions with and without censoring. The tests considered include the F test, a modification for samples that are from Weibull distributions, Cox's test, Peto & Peto's log rank test, their generalized Wilcoxon test, a modified log rank test, and a generalized Wilcoxon test of Gehan. When samples are from exponential distributions, with or without censoring, the F test is the most powerful followed by two general groupings of tests: first the three non-Wilcoxon tests and then the two Wilcoxon tests. There is little difference in the power characteristics of the tests within each grouping. Estimates of the asymptotic relative efficiencies of the various tests relative to F are obtained from the normal probability plots of the power curves. When the samples are taken from Weibull distributions with constant hazard ratio, the F test is not robust and a modification is used. The results for this case are essentially the same as in the case of exponential distributions, with the modified F test as the most powerful. However, when the hazard ratio is nonconstant, the two generalizations of the Wilcoxon test have more power than the other tests.

Key Words: Asymptotic relative efficiency • Censoring • Monte Carlo study • Nonparametric two-sample tests • Power curves • Probability plot • Weibull distribution • Wilcoxon test


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?




Disclaimer:
Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.