Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
415085 | Computational Statistics & Data Analysis | 2011 | 10 Pages |
Abstract
In a two-sample location-scale model with censored data, the logrank test is asymptotically efficient when the error distribution is extreme minimum value. On the other hand, the Wilcoxon test is asymptotically efficient when the error distribution is logistic. We propose a pretest for choosing between logrank and Wilcoxon by determining if the error distribution is closer to extreme minimum value or logistic. This adaptive test is compared with the logrank and Wilcoxon tests through simulation.
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
Annie Tordilla Darilay, Joshua D. Naranjo,