A Note on Empirical Likelihood Inference of Residual Life Regression

Ying Chen and Yichuan Zhao
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Ying Chen: Division of Biostatistics, School of Public Health, University of California, Berkeley
Yichuan Zhao: Department of Mathematics and Statistics, Georgia State University, Atlanta, Georgia

No 1153, U.C. Berkeley Division of Biostatistics Working Paper Series from Berkeley Electronic Press

Abstract: Mean residual life function, or life expectancy, is an important function to characterize distribution of residual life. The proportional mean residual life model by Oakes and Dasu (1990) is a regression tool to study the association between life expectancy and its associated covariates. Although semiparametric inference procedures have been proposed in the literature, the accuracy of such procedures may be low when the censoring proportion is relatively large. In this paper, the semiparametric inference procedures are studied with an empirical likelihood ratio method. An empirical likelihood confidence region is constructed for the regression parameters. The proposed method is further compared with the normal approximation based method through a simulation study.

Keywords: Confidence region; counting process; estimating equation; proportional mean residual life model; right-censoring; Wilks's theorem (search for similar items in EconPapers)
Date: 2004-07-11
Note: oai:bepress.com:ucbbiostat-1153
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