Abstract Details
Activity Number:
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240
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Type:
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Contributed
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Date/Time:
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Monday, August 5, 2013 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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Abstract - #308032 |
Title:
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Dual-Likelihood Ratio Test for Type-I Censored Multiple Samples Under Semiparametric Density Ratio Models
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Author(s):
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Song Cai*+ and Jiahua Chen
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Companies:
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University of British Columbia and Universithy of British Columbia
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Keywords:
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density ratio model ;
emprical likelihood ;
censoring ;
semiparametric
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Abstract:
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Density ratio model (DRM) is a flexible semiparametric model for the relationships among the densities of the underlying distributions of multiple samples. Empirical likelihood (EL) is a powerful tool for the inference of the DRM parameters. This paper establish EL theory for the inference of DRM parameters when the observations are type--I censored, e.g. the experiment stopped at a pre--specified level. We show that the maximum empirical likelihood estimator of the DRM parameter is identical to a maximum partial dual--empirical likelihood estimator. We also show that the corresponding dual--likelihood ratio statistic has a simple Chisquared limiting distribution under a class of general composite null hypothesis about the DRM parameters. This result is especially useful for testing the equality of entire underlying distributions of many different independent samples with type--I censored observations.
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Authors who are presenting talks have a * after their name.
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