Nonparametric estimation of the conditional mean residual life function with censored data |
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Authors: | Alexander C McLain Sujit K Ghosh |
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Institution: | Division of Epidemiology, Statistics and Prevention Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD 20892, USA. mclaina@mail.nih.gov |
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Abstract: | The conditional mean residual life (MRL) function is the expected remaining lifetime of a system given survival past a particular
time point and the values of a set of predictor variables. This function is a valuable tool in reliability and actuarial studies
when the right tail of the distribution is of interest, and can be more informative than the survivor function. In this paper,
we identify theoretical limitations of some semi-parametric conditional MRL models, and propose two nonparametric methods
of estimating the conditional MRL function. Asymptotic properties such as consistency and normality of our proposed estimators
are established. We investigate via simulation study the empirical properties of the proposed estimators, including bootstrap
pointwise confidence intervals. Using Monte Carlo simulations we compare the proposed nonparametric estimators to two popular
semi-parametric methods of analysis, for varying types of data. The proposed estimators are demonstrated on the Veteran’s
Administration lung cancer trial. |
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Keywords: | |
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