Inverse censoring weighted median regression

Document Type

Article

Publication Date

11-1-2009

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. © 2009 Elsevier B.V. All rights reserved.

Identifier

70449527531 (Scopus)

Publication Title

Statistical Methodology

External Full Text Location

https://doi.org/10.1016/j.stamet.2009.06.006

ISSN

15723127

First Page

594

Last Page

603

Issue

6

Volume

6

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