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Abstract Details
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Activity Number:
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519
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Type:
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Contributed
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Date/Time:
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Wednesday, August 3, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Business and Economic Statistics Section
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| Abstract - #301666 |
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Title:
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A Semiparametric Approach for Multivariate Longitudinal Count Data
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Author(s):
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Darcy Steeg Morris*+
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Companies:
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Cornell University
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Address:
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171 East State Street, Ithaca, NY, 14850,
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Keywords:
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Generalized Estimating Equations ;
Correlated Count Data ;
Generalized Linear Mixed Models ;
Unobserved Heterogeneity ;
Longitudinal Data Analysis
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Abstract:
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A semiparametric method for estimating the marginal regression and association parameters in a multivariate longitudinal random effects count model is considered. Using the generalized estimating equation (GEE) framework, a specific form of the covariance matrix of the response vector is imposed which induces dependence over time and outcomes using random effects. This moment based method reduces the computational burden associated with a high-dimensional joint distribution by avoiding parametric assumptions on the response and unobserved effects. Through a simulation study, performance of the semiparametric estimators is compared with estimators from a pairwise likelihood approach. Both of these methods are then used to analyze a dataset of insurance claim counts for three types of coverage over time.
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Authors who are presenting talks have a * after their name.
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