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Abstract Details
Activity Number:
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317
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Type:
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Invited
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Date/Time:
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Tuesday, August 2, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Business and Economic Statistics Section
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Abstract - #300304 |
Title:
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A Flexible Hierarchical Approach to Modeling Discrete-Valued Spatio-Temporal Data
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Author(s):
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Scott H. Holan*+ and Christopher K. Wikle
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Companies:
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University of Missouri and University of Missouri
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Address:
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Department of Statistics, Columbia , MO, 65211,
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Keywords:
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Bayesian hierarchical models ;
counts ;
overdispersion ;
spatially-varying
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Abstract:
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In many cases modeling discrete-valued spatio-temporal data is a straightforward endeavor. However, in many real-world applications the complexities of the data and/or process don't allow for routine model specification. For example, often discrete-valued spatio-temporal data exhibit zero-inflation, over/under dispersion or heavy tails and contain many sources of uncertainty. In order to accommodate such structure, while quantifying different sources of uncertainty, we propose a hierarchical Bayesian model that utilizes a flexible likelihood specification. The approach we propose allows the likelihood to adapt to the nuances of the discrete-valued data while flexibly accommodating different spatio-temporal dependence structures. The effectiveness of our methodology is demonstrated through simulation and through a real-data application.
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