Empirical Likelihood

Document Type

Presentation Abstract

Presentation Date

11-30-2006

Abstract

Empirical likelihood (EL) is a nonparametric inference method with asymptotic properties that are in general similar to the parametric maximum likelihood. For example, there are EL versions of likelihood ratio tests and of Wilks' theorem. As its parametric equivalent, the EL approach often provides an efficient and practical inference tool in situations where other inferential methods do not succeed. In this presentation, I will first give a general introduction to empirical likelihood. Then, I plan to demonstrate a new procedure for combining multiple tests in samples of right-censored observations. The new method is based on multiple constraint censored empirical likelihood where the constraints are formulated as linear functionals of the cumulative hazard functions. A useful application of the proposed method is examining the survival experience of one or more populations by combining different weighted log-rank tests. Real data examples are given using the log-rank and Gehan-Wilcoxon tests. Simulation results demonstrate that, in addition to its computational simplicity, the combined test performs comparably to, and in some situations more reliably than previously developed procedures.

Additional Details

Thursday, 30 November 2006
4:10 p.m. in Math 109

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