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Activity Number:
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128
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Type:
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Invited
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Date/Time:
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Monday, July 30, 2007 : 10:30 AM to 12:20 PM
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Sponsor:
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JASA, Theory and Methods
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| Abstract - #307718 |
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Title:
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Implementation of Estimating-Function--Based Inference Procedures with MCMC Samplers
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Author(s):
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Lee-Jen Wei*+ and Lu Tian and Jun S. Liu
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Companies:
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Harvard University and Northwestern University and Harvard University
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Address:
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Department of Biostatistics, HSPH, Boston, MA, 02115,
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Keywords:
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Bootstrap ; Median regression ; Metropolis algorithm ; Normal approximation ; Resampling ; Survival analysis
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Abstract:
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Under a semiparametric or nonparametric setting, inferences about the unknown parameter are often made based on a non-smooth estimating function. Resampling methods are quite handy for obtaining good approximations to the distribution of the consistent estimator when the estimating equation and its resampled counterparts are not difficult to solve numerically. In this paper, we propose a simple, flexible procedure which provides such approximations via the standard Markov chain Monte Carlo sampler without solving any equations. More generally the procedure may locate all possible roots of the estimating equation and provides an approximation to the distribution of each root. We illustrate our proposal extensively with three examples. The performance of the new procedure is also examined comprehensively via a simulation study.
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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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