Abstract Details
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Activity Number:
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376
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Type:
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Contributed
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Date/Time:
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Tuesday, August 6, 2013 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Computing
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| Abstract - #310400 |
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Title:
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Monte Carlo Maximum Likelihood for the Two-Stage Hierarchical Model
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Author(s):
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Christina Knudson*+
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Companies:
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University of Minnesota
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Keywords:
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Monte Carlo ;
maximum likelihood ;
likelihood approximation
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Abstract:
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The likelihood for a two-stage hierarchical model is often difficult or impossible to compute due to the model's unobserved random effects. These random effects require an integral, which may be of many dimensions. Monte Carlo Maximum Likelihood approximates the likelihood using random effects simulated according to a user-specified distribution. This approximation can then be maximized to find Monte Carlo maximum likelihood estimates and their corresponding standard errors.
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Authors who are presenting talks have a * after their name.
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