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Activity Number:
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246
- Bayesian Nonparametrics
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Type:
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Contributed
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Date/Time:
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Monday, July 29, 2019 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract #307198
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Title:
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Bayesian Spatial Nonhomogeneous Poisson Process Based on Mixture of Finite Mixtures Model with Applications
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Author(s):
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Wei Shi* and Junxian Geng and Guanyu Hu
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Companies:
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University of Connecticut and Boehringer Ingelheim and University of Connecticut
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Keywords:
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MCMC;
Earthquake;
Nonparametric Bayesian;
Dirichlet Process
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Abstract:
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Intensity estimation is a common problem in statistical analysis of spatial point patterns. Nonhomogeneous Poisson process model is the most frequently used model for spatial point pattern data. This paper proposes a nonparametric Bayesian method for estimating the point process intensity based on mixture of finite mixture (MFM) model. MFM approach leads to a consistent estimate of the number of clusters of the intensity of spatial point patterns in different area. An efficient Markov chain Monte Carlo (MCMC) algorithm is proposed for our method. Extensive simulation studies are carried out to examine empirical performance of the proposed method. The usage of our proposed method is further illustrated with the analysis of the Earthquake Hazards Program of United States Geological Survey (USGS) earthquake data.
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Authors who are presenting talks have a * after their name.
