Abstract Details
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Activity Number:
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171
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Type:
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Contributed
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Date/Time:
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Monday, August 4, 2014 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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Abstract #311840
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View Presentation
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Title:
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Efficient Semiparametric Estimators for Proportional Hazards Models with Measurement Error
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Author(s):
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Yuhang Xu*+ and Yehua Li and Xiao Song
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Companies:
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Iowa State University and Iowa State University and University of Georgia
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Keywords:
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Efficient score ;
Measurement error ;
Survival analysis
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Abstract:
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A new class of semiparametric estimators is proposed for proportional hazards models in the presence of measurement error in the covariates. Both the baseline hazard function and the distribution of the true covariates are modeled as unknown infinite dimensional parameters. The proposed estimators are locally efficient in the sense that estimators are semiparametric efficient if the distribution of the error-prone covariates is specified correctly and are still consistent and asymptotic normal if this distribution is misspecified. Our simulation studies show that the proposed estimators have smaller variances than those of the competing methods.
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Authors who are presenting talks have a * after their name.
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