Abstract Details
Activity Number:
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18
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Type:
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Topic Contributed
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Date/Time:
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Sunday, August 3, 2014 : 2:00 PM to 3:50 PM
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Sponsor:
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Survey Research Methods Section
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Abstract #311322
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Title:
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Flexible Bayesian Methodology for Multivariate Spatial Small Area Estimation
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Author(s):
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Aaron T. Porter*+ and Scott Holan and Christopher K. Wikle
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Companies:
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University of Missouri and University of Missouri and University of Missouri
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Keywords:
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American Community Survey ;
Areal Data ;
Bayesian Hierarchical Model ;
Multivariate
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Abstract:
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The importance of explicitly accounting for spatial correlation in univariate small area estimation has recently been acknowledged. However, methodology for simultaneously and explicitly modeling within-area correlation and between-area spatial correlation in multivariate small area estimation is relatively under-developed. In this research, we develop hierarchical Bayesian methodology to flexibly handle explicit multivariate spatial dependence in the Fay-Herriot framework of models. The effectiveness of our approach is illustrated through simulation as well as data from the American Community Survey.
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Authors who are presenting talks have a * after their name.
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