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Doctoral Dissertation Announcement
Candidate: Annie Tordilla
Degree of:
Doctor of Philosophy
Department: Statistics
Title: A Two-Sample Adaptive Procedure Based on the Log-Rank and Peto and Peto’s Wilcoxon Tests
Committee:
Dr. Joshua Naranjo, Chair
Dr. Joseph McKean
Dr. Jung-Chao Wang
Dr. Steven Denham
Date: Thursday, September 10, 2009 9:00 a.m. - 11:00 a.m.
6625 Everett Tower
Abstract:
It has been shown that, under a location-scale model y = μ + βz + σε, where y is right censored, the Log-Rank test is asymptotically efficient for the Extreme minimum error distribution while Peto and Peto's Wilcoxon test is asymptotically efficient for the Logistic error distribution. We propose a two-sample adaptive test, which first selects between Extreme minimum value and Logistic error distribution as to which is a better fit to the data, then performs the asymptotically efficient test (Log-Rank or Peto and Peto's Wilcoxon test) for the selected distribution. The performance of the adaptive test is compared with the Log-Rank and Peto and Peto's Wilcoxon tests through simulation.