Nonparametric likelihood ratio goodness-of-fit tests for survival data

Gang Li

Journal of Multivariate Analysis, 2003, vol. 86, issue 1, 166-182

Abstract: Berk and Jones (Z. Wahrsch. Verw. Gebiete 47 (1979) 47) described a nonparametric likelihood test of uniformity that is more efficient, in Bahadur's sense, than any weighted Kolmogorov-Smirnov test at any alternative. This article shows how to obtain a nonparametric likelihood test of a general parametric family for incomplete survival data. A nonparametric likelihood ratio test process is employed to measure the discrepancy between a parametric family and the observed data. Large sample properties of the likelihood ratio test process are studied under both the null and alternative hypotheses. A Monte Carlo simulation method is proposed to estimate its null distribution. We show how to produce a likelihood ratio graphical check as well as a formal test of a parametric family based on the developed theory. Our method is developed for the right-censorship model, but can be easily extended to some other survival models. Illustrations are given using both real and simulated data.

Keywords: Censoring; Kolmogorov-type; test; Martingale; Monte; Carlo; simulation; Truncation (search for similar items in EconPapers)
Date: 2003
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Citations: View citations in EconPapers (4)

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