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Activity Number:
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64
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Type:
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Contributed
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Date/Time:
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Sunday, July 29, 2007 : 4:00 PM to 5:50 PM
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Sponsor:
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Biometrics Section
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| Abstract - #309437 |
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Title:
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Solving Structured Maximum Likelihood Estimating Equations for Nested Marginal Discrete Longitudinal Models
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Author(s):
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Arunava Chakravartty*+ and Subir Ghosh
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Companies:
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University of California, Riverside and University of California, Riverside
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Address:
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200 W Big Springs Road, Riverside, CA, 92507,
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Keywords:
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estimating equations ; marginal models ; longitudinal data ; nested models ; numerical convergence ; intial solution
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Abstract:
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Three nested marginal models are considered for describing a discrete longitudinal data. The Maximum Likelihood Estimating Equations for these three models have a common set of Estimating Equations with a special structure. This paper brings forth the structure common to these set of Estimating Equations and then describes the methods for solving the estimating equations for all three nested models. The initial solutions in these methods are determined naturally from the Estimating Equations and then improved to the final solutions through a series of steps described in the paper.
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