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Activity Number:
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569
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Type:
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Contributed
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Date/Time:
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Thursday, August 6, 2009 : 8:30 AM to 10:20 AM
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Sponsor:
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Biometrics Section
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| Abstract - #305600 |
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Title:
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Robust Variable and Model Selection with Missing Data
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Author(s):
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Greg DiRienzo*+
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Companies:
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State University of New York at Albany
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Address:
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School of Public Health, Rensselaer, NY, 12144,
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Keywords:
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Average prediction error ; Doubly-robust estimator ; Model misspecification ; Restricted moment model
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Abstract:
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A semi-recursive stochastic approximation to an idealized algorithm for estimating the most parsimonious generalized linear model with missing data and multiple covariates is studied. Simulation studies reveal robust properties of the approach under the missing at random assumption. Additionally, the method appears adequate for detecting interaction terms. Analysis of baseline and longitudinal measurements of CD4 count, HIV-1 genotype, RNA level is provided.
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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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