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Activity Number:
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15
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Type:
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Topic Contributed
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Date/Time:
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Sunday, August 6, 2006 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #306677 |
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Title:
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Statistical Inference for Nonlinear Models Involving Ordinary Differential Equations
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Author(s):
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Lovely Goyal*+ and Sujit Ghosh
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Companies:
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North Carolina State University and North Carolina State University
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Address:
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2435 Wycliff Road, Apt. H, Raleigh, NC, 27607,
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Keywords:
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Bayesian ; differential equations ; MCMC ; nonlinear models ; fixed effect
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Abstract:
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In the context of nonlinear fixed-effect modeling, it is common to describe the relationship between a response variable and a set of explanatory variables by a system of nonlinear ordinary differential equations (ODEs). More often, such a system of ODEs does not have any analytical closed-form solution, making parameter estimation for these models challenging and computationally demanding. Two new methods based on Euler's approximation are proposed to obtain an approximate likelihood that is analytically tractable, thus making parameter estimation computationally less demanding than other methods. These methods are illustrated using data on growth colonies of Paramecium aurelium. Simulation studies are presented to compare the performances of these new methods to established methods in the literature.
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