This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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424
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Type:
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Contributed
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Date/Time:
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Tuesday, August 3, 2010 : 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 - #308379 |
Title:
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On Semiparametric Exponential Tilt Regression Models
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Author(s):
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Alan Huang*+ and Paul J. Rathouz
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Companies:
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The University of Chicago and The University of Chicago
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Address:
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200 W. Hill St, Chicago, IL, 60610,
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Keywords:
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exponential tilting ;
semiparametric model ;
likelihood ratio test ;
generalized linear models
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Abstract:
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A semiparametric exponential tilt regression model was introduced by Rathouz & Gao (2009) as a semiparametric generalization of generalized linear models (see, e.g., McCullagh & Nelder, 1989). This approach differs from quasi-likelihood (Wedderburn, 1974) in that it treats the error distribution as a completely unspecified infinite-dimensional parameter in the likelihood function. I will present conditions under which profiling out the error distribution in the likelihood function results in a profile likelihood that behaves asymptotically like the true likelihood for the parameters in the mean model, thereby obtaining asymptotically normal estimators for mean parameters that are also asymptotically efficient. I will also comment on how inferences can be made on the nonparametric component.
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