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Activity Number:
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338
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Type:
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Contributed
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Date/Time:
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Tuesday, July 31, 2007 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Nonparametric Statistics
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| Abstract - #309471 |
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Title:
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Conditional Empirical Likelihood Inference in Presence of Nuisance Parameters
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Author(s):
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Mi-Ok Kim*+
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Companies:
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Cincinnati Children's Hospital Medical Center
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Address:
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MLC 5041 3333 Burnet Ave, Cincinnati, OH, 45229-3039,
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Keywords:
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empirical likelihood ; nuisance parameter ; censored data
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Abstract:
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Empirical likelihood (EL) is a nonparametric inference method with results that are in general similar to those about likelihood ratio tests and Wilk's theorem in the parametric model. In this talk we formulate EL inference in the frame work of ordinary parametric likelihood using a parameterized sub-family of distributions and discuss its inference. In particular we discuss EL inference in presence of nuisance parameters. We propose an approximate conditional EL inference and extend this proposition to censored case.
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