Nancy Glenn, Ph.D. Candidate
Dissertation Topic: Robust Empirical Likelihood
My research develops a new nonparametric technique, robust empirical likelihood, that employs the empirical likelihood method to construct robust confidence intervals for non-robust statistics. The technique invokes constrained optimization to maximize a robust version of the empirical likelihood function, allowing data analysts to estimate parameters accurately despite any potential contamination.
Robust empirical likelihood builds upon the empirical likelihood method introduced by Dr. Art Owen of Stanford University.
Both empirical likelihood methods combine 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 extends to confidence regions and to all disciplines that use likelihood methods.
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I presented Robust Empirical Likelihood at the
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Dr. Owen's Empirical Likelihood Book
You may now order the first book ever written on the topic Empirical Likelihood. It's creatively entitled
Empirical Likelihood. I helped proofread it, so I can say first hand that it's a very well written book!