Empirical likelihood inference for median regression models for censored survival data

Authors:
Gengsheng Qin;Min Tsao
Affiliations:
Georgia State University, Atlanta, GA;Department of Mathematics & Statistics, University of Victoria, P.O. Box 3045, Victoria, BC, Canada V8W 3P4
Venue:
Journal of Multivariate Analysis
Year:
2003

Citing 2
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Empirical likelihood inference for censored median regression model via nonparametric kernel estimation

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Support vector censored quantile regression under random censoring

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Two-step generalised empirical likelihood inference for semiparametric models

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Empirical likelihood for median regression model with designed censoring variables

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Abstract

Recent advances in median regression model have made it possible to use this model for analyzing a variety of censored survival data. For inference on the model parameter vector, there are now semiparametric procedures based on normal approximation that are valid without strong conditions on the error distribution. However, the accuracy of such procedures can be quite low when the censoring proportion is high. In this paper, we propose an alternative semiparametric procedure based on the empirical likelihood. We define the empirical likelihood ratio for the parameter vector and show that its limiting distribution is a weighted sum of chi-square distributions. Numerical results from a simulation study suggest that the empirical likelihood method is more accurate than the normal approximation based method of Ying et al. (J. Amer. Statist. Assoc. 90 (1995) 178).