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Activity Number:
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412
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 1, 2007 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Nonparametric Statistics
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| Abstract - #308845 |
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Title:
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Generalized Semiparametric Linear Mixed Effects Models
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Author(s):
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Tatiyana Apanasovich*+
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Companies:
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Cornell University
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Address:
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228 Rhodes Hall, Ithaca, NY, 14850,
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Keywords:
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Abstract:
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Generalized linear mixed effects models are widely used for longitudinal non-Gaussian data analysis to incorporate between-subject and within-subject variations. To weaken model assumption for possible misspecification and to avoid the curse of dimensionality of fully nonparametric regression in the presence of several predictor variables, semiparametric models have been considered. We employ the penalized likelihood regression to estimate the models. We will focus on the efficient computation and the effective smoothing parameter selection. Real-data examples from AIDS studies will be presented to demonstrate the applications of the methodology.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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