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Activity Number: 316 - Emerging Advances of Innovative Computational Skills with Unconventional Likelihoods
Type: Invited
Date/Time: Tuesday, July 30, 2019 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Computing
Abstract #300031
Title: Maximum Empirical Likelihood Estimation and Related Topics
Author(s): Anton Schick*
Companies: Binghamton University

A theory of maximum empirical likelihood estimation and empirical likelihood ratio testing with irregular and estimated constraint functions is presented that parallels the theory for parametric models and is tailored for semiparametric models. The key is a uniform local asymptotic normality condition for the local empirical likelihood ratio. This condition is shown to hold under mild assumptions on the constraint function. Applications are discussed to inference problems about quantiles under possibly additional information on the underlying distribution and to residual-based inference about quantiles.

Authors who are presenting talks have a * after their name.

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