This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
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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 3, 2010 : 10:30 AM to 12:20 PM
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Sponsor:
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Statistical and Applied Mathematical Sciences Institute
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| Abstract - #306038 |
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Title:
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Multivariate Zero-Inflated Bayesian Spatial Model
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Author(s):
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Chong He*+ and Jing Zhang
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Companies:
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University of Missouri and Miami University
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Address:
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307C Middlebush Hall, Columbia, MO, 65211,
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Keywords:
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zero-inflated data ;
Bayesian method ;
spatial model ;
multivariate
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Abstract:
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A data set is referred to as zero-inflated when there are excess zeros in the data set that it does not readily fit any standard distribution. Recently there has been increasing interest in analyzing the zero-inflated spatial count data, while less attention has been paid to the zero-inflated spatial continuous data. The zero-inflated spatial continuous data is often observed in environmental and ecological study due to limit of detection. We develop a Bayesian hierarchical model to analyze multivariate zero-inflated spatial continuous data. The approach is illustrated via an application to the herbaceous data collected in the Missouri Ozark Forest Ecosystem Project (MOFEP).
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