This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
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Activity Number:
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75
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Type:
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Contributed
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Date/Time:
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Sunday, August 1, 2010 : 4:00 PM to 5:50 PM
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Sponsor:
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ENAR
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| Abstract - #307502 |
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Title:
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Bayesian Influence Methods with Missing Covariates in Survival Analysis
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Author(s):
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Diana Lam*+ and Joseph G. Ibrahim and Hongtu Zhu
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Companies:
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The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill
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Address:
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UNC Chapel Hill, Dept of Biostatistics, Chapel Hill, NC, 27599,
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Keywords:
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diagnostics ;
missingness ;
survival models ;
Bayesian influence methods
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Abstract:
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In this talk we formally develop general Bayesian local and global influence methods to carry out sensitivity analyses of perturbations to survival models in the presence of missing covariate data. We examine several types of perturbation schemes for perturbing various assumptions in this setting. In doing so, we show that the metric tensor of a Bayesian perturbation manifold provides useful information for selecting an appropriate perturbation. We also develop several Bayesian local influence measures to identify influential points, assess model assumptions and examine robustness of the proposed model. Simulation studies are conducted to evaluate our methods, and real datasets are analyzed to illustrate the use of our influence measures.
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Authors who are presenting talks have a * after their name.
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