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Activity Number:
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182
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Type:
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Invited
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Date/Time:
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Monday, July 30, 2007 : 2:00 PM to 3:50 PM
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Sponsor:
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SSC
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| Abstract - #308121 |
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Title:
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Second-Order Least Squares Estimation for Nonlinear Mixed Effects Models
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Author(s):
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Liqun Wang*+
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Companies:
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University of Manitoba
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Address:
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Department of Statistics, Winnipeg, MB, R3T 2N2, Canada
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Keywords:
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Semiparametric model ; non-normal random effects ; least squares method ; simulation-based estimation ; longitudinal data ; exact consistency
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Abstract:
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The paper deals with nonlinear mixed effects models where the random effects have a general parametric distribution (not necessarily normal) and the distributions of other variables and random errors are nonparametric. I propose a second-order least squares estimator based on the first two marginal moments of the response variable. I also propose a simulation-based estimator when the closed forms of the marginal moments are not available. Both estimators are consistent and asymptotically normally distributed under fairly general conditions. Monte Carlo simulation studies show that the proposed estimators perform well for relatively small sample sizes. Compared to the likelihood approach, the new methods are computationally feasible and do not rely on the normality assumption.
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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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