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Activity Number: 127
Type: Topic Contributed
Date/Time: Monday, August 4, 2008 : 10:30 AM to 12:20 PM
Sponsor: Section on Survey Research Methods
Abstract - #301317
Title: Assessing Synthetic Error via Markov Chain Monte Carlo Techniques
Author(s): Andrew Keller*+
Companies: U.S. Census Bureau
Address: 4600 Silver Hill Rd., Washington, DC, 20233,
Keywords:
Abstract:

Many small domain estimates require a precise, direct estimate of the within domain variability as one component. However, due to small sample size, the precision of within small domain direct variance estimates of census coverage is questionable. In this paper, Markov chain Monte Carlo (MCMC) techniques are applied to develop a model-based estimate of the within domain variability as part of the estimation process. For this particular application, variability within state is modeled via a random effects model where the census block is the replicate. Ultimately, this block-level model is applied to evaluate synthetic error of the small domain.

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