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Abstract Details

Activity Number: 143
Type: Contributed
Date/Time: Monday, August 2, 2010 : 8:30 AM to 10:20 AM
Sponsor: Section on Survey Research Methods
Abstract - #308496
Title: Bayesian Quantile Regression in Stratified Sampling
Author(s): Nanhua Zhang*+ and Michael R. Elliott
Companies: University of Michigan and University of Michigan
Address: 1420 Washington Heights, 4th FL, Ann Arbor, MI, 48109,
Keywords: Gibbs sampling ; complex survey ; disproportional sampling ; variance estimates
Abstract:

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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