Date of Award

12-2009

Degree Name

Doctor of Philosophy

Department

Statistics

First Advisor

Dr. Joshua Naranjo

Second Advisor

Dr. Joseph McKean

Third Advisor

Dr. Jung-Chao Wang

Fourth Advisor

Dr. Steven Denham

Abstract

It has been shown that under a location-scale model y = μ + βz + σε at where y is right censored, the Log-Rank test is asymptotically efficient for the Extreme minimum value 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.

Access Setting

Dissertation-Open Access

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