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Activity Number: 524
Type: Topic Contributed
Date/Time: Wednesday, August 3, 2016 : 10:30 AM to 12:20 PM
Sponsor: Survey Research Methods Section
Abstract #319867
Title: Small-Area Estimation for High-Dimensional Non-Gaussian Dependent Data
Author(s): Jonathan R. Bradley* and Scott H. Holan and Christopher Wikle
Companies: University of Missouri and University of Missouri and University of Missouri
Keywords: Markov chain Monte Carlo ; Multivariate spatio-temporal data ; Big Data ; Multiscale ; Hierarchical model ; Small area estimation
Abstract:

We introduce a Bayesian approach for analyzing high-dimensional dependent data that are distributed according to a member from the exponential family of distributions. This problem requires extensive methodological advancements, as jointly modeling high-dimensional dependent data leads to the so-called "big n problem." The computational complexity of this problem is further exacerbated by allowing for non-Gaussian data models. Thus, we develop new computationally efficient distribution theory for this setting. In particular, we introduce a class of conjugate multivariate distributions for the exponential family. We discuss several theoretical results regarding conditional distributions, an asymptotic relationship with the multivariate normal distribution, parameter models, and full-conditional distributions for a Gibbs sampler. We demonstrate the modeling framework through several examples, including an analysis of a large dataset consisting of American Community Survey (ACS) period estimates.


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