This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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143
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Type:
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Contributed
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Date/Time:
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Monday, August 2, 2010 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Survey Research Methods
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Abstract - #308496 |
Title:
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Bayesian Quantile Regression in Stratified Sampling
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Author(s):
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Nanhua Zhang*+ and Michael R. Elliott
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Companies:
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University of Michigan and University of Michigan
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Address:
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1420 Washington Heights, 4th FL, Ann Arbor, MI, 48109,
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Keywords:
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Gibbs sampling ;
complex survey ;
disproportional sampling ;
variance estimates
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Abstract:
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Quantile regression has gained much popularity in recent years. No method, except for bootstrap, has been proposed to estimate the variance in quantile regression in complex survey setting. We propose a Bayesian approach for quantile regression in stratified sampling, using a hierarchical model. This approach is easily implemented using a Gibbs sampling algorithm. It yields estimate that is design-consistent. We also discuss extensions of our method to other sampling schemes such as PPS sampling.
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