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dc.contributor.authorJaeger, Adam Paul
dc.date.accessioned2015-10-23T04:30:32Z
dc.date.available2015-10-23T04:30:32Z
dc.date.issued2015-05
dc.identifier.otherjaeger_adam_p_201505_phd
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/jaeger_adam_p_201505_phd
dc.identifier.urihttp://hdl.handle.net/10724/33079
dc.description.abstractThe likelihood function plays a pivotal role in statistical inference; it is adaptable to a wide range of models and the resultant estimators are known to have good properties. However, these results hinge on correct specification of the true data generating mechanism. Many modern problems involve extremely complicated distribution functions, which may be difficult -- if not impossible -- to express explicitly. This is a serious barrier to the likelihood approach, which requires not only the specification of a distribution, but the correct distribution. Non-parametric methods are one way to avoid the problem of having to specify a particular data generating mechanism, but can be computationally intensive, reducing their accessibility for large data problems. We propose a new approach that combines multiple non-parametric likelihood-type components to build a data-driven approximation of the true function. We build on two alternative likelihood approaches, empirical and composite likelihood, taking advantage of the strengths of each. Specifically, from empirical likelihood we borrow the ability to avoid a parametric specification, and from composite likelihood we gain a decrease in computational load. We will examine the theoretical properties of this new construct, both for purposes of application and to compare properties to other established likelihood methods.
dc.languageeng
dc.publisheruga
dc.rightspublic
dc.subjectEstimating equations
dc.subjectInference
dc.subjectLikelihood
dc.subjectNon-parametric
dc.subjectRobust
dc.titleComposite empirical likelihood
dc.title.alternativea derivation of multiple non-parametric likelihoods
dc.typeDissertation
dc.description.degreePhD
dc.description.departmentStatistics
dc.description.majorStatistics
dc.description.advisorNicole Lazar
dc.description.committeeNicole Lazar
dc.description.committeeCheolwoo Park
dc.description.committeeWilliam P. McCormick
dc.description.committeeDaniel Hall
dc.description.committeeJeongyoun Ahn


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