Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1151035 | Statistical Methodology | 2009 | 10 Pages |
Abstract
We implement semiparametric random censorship model aided inference for censored median regression models. This is based on the idea that, when the censoring is specified by a common distribution, a semiparametric survival function estimator acts as an improved weight in the so-called inverse censoring weighted estimating function. We show that the proposed method will always produce estimates of the model parameters that are as good as or better than an existing estimator based on the traditional Kaplan–Meier weights. We also provide an illustration of the method through an analysis of a lung cancer data set.
Related Topics
Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
Sundarraman Subramanian, Gerhard Dikta,