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A rank estimator in the two-sample transformation model with randomly censored data

✍ Scribed by Hideatsu Tsukahara

Publisher
Springer Japan
Year
1992
Tongue
English
Weight
818 KB
Volume
44
Category
Article
ISSN
0020-3157

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✦ Synopsis

We consider the transformation model which is a generalization of Lehmann alternatives model. This model contains a parameter 0 and a nonparametric part F1 which is a distribution function. We propose a kind of M-estimator of 8 based on ranks in the presence of random censoring. It is nonparametric in the sense that we do not have to know F1. Moreover, it is simple and asymptotically normal. For the proportional hazards model with special censoring, we obtain the asymptotic relative efficiency of our estimator with respect to the best nonparametric estimator for this model. It is quite efficient for special values of 8. We also make a comparison between our estimator and other proposed estimators with real data.

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