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Activity Number: 400
Type: Contributed
Date/Time: Tuesday, August 5, 2014 : 2:00 PM to 3:50 PM
Sponsor: Section on Nonparametric Statistics
Abstract #312149 View Presentation
Title: Composite Empirical Likelihood: A Derivation of Multiple Nonparametric Likelihoods
Author(s): Adam Jaeger*+ and Nicole Lazar
Companies: University of Georgia and University of Georgia
Keywords: Non parametric ; composite likelihood ; empirical likelihood ; robust estimation
Abstract:

The likelihood function is a fundamental part of statistical inference, providing the basis for both estimation and testing. The development of approximations to the true likelihood function allow for application to problems where the true parametric likelihood cannot be expressed or is computationally intractable. Two general groups of "approximate" likelihoods are the composite and empirical. The composite form consists of pieces of true likelihoods which are appropriately combined, whereas empirical likelihood is a non parametric construct. We propose combining these two method to develop a new class, which we call a composite empirical likelihood, which as the name suggests, comprises pieces of empirical likelihoods. We define the general form of a composite empirical likelihood, explore the asymptotic properties of this new class and explore several applications of this approach.


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