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dc.contributor.advisor Scott, David W.
dc.contributor.advisor Zhang, Yin
dc.creatorGlenn, Nancy Louise
dc.date.accessioned 2009-06-04T06:38:03Z
dc.date.available 2009-06-04T06:38:03Z
dc.date.issued 2002
dc.identifier.urihttps://hdl.handle.net/1911/18083
dc.description.abstract This research introduces a new nonparametric technique: robust empirical likelihood. Robust empirical likelihood employs the empirical likelihood method to compute robust parameter estimates and confidence intervals. The technique uses constrained optimization to solve a robust version of the empirical likelihood function, thus allowing data analysts to estimate parameters accurately despite any potential contamination. Empirical likelihood combines the utility of a parametric likelihood with the flexibility of a nonparametric method. Parametric likelihoods are valuable because they have a wide variety of uses; in particular, they are used to construct confidence intervals. Nonparametric methods are flexible because they produce accurate results without requiring knowledge about the data's distribution. Robust empirical likelihood's applications include regression models, hypothesis testing, and all areas that use likelihood methods.
dc.format.extent 49 p.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.subjectStatistics
dc.title Robust empirical likelihood
dc.type.genre Thesis
dc.type.material Text
thesis.degree.department Statistics
thesis.degree.discipline Engineering
thesis.degree.grantor Rice University
thesis.degree.level Doctoral
thesis.degree.name Doctor of Philosophy
dc.identifier.citation Glenn, Nancy Louise. "Robust empirical likelihood." (2002) Diss., Rice University. https://hdl.handle.net/1911/18083.


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