JSM 2005 - Toronto

Abstract #304727

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 362
Type: Contributed
Date/Time: Wednesday, August 10, 2005 : 8:30 AM to 10:20 AM
Sponsor: Section on Nonparametric Statistics
Abstract - #304727
Title: Weighted Empirical Likelihood Estimates and Their Robustness Properties
Author(s): Nancy Glenn*+
Companies: University of South Carolina
Address: 1148 Rockwood Rd, Columbia, SC, 29209, United States
Keywords: Empirical Likelihood ; Contaminated Normal ; EM Algorithm
Abstract:

This research introduces a nonparametric likelihood function for data that are independent, but not necessarily identically distributed. We consider the family of contaminated normal densities and show that weighted empirical likelihood is more robust than empirical likelihood. The technique employs the expectation-maximization algorithm to choose appropriate weight vectors that weigh the data's contribution to the log-likelihood function, then employs constrained optimization to obtain parameter estimates.

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