New Development of Bayesian Variable Selection Criteria for Spatial\n Point Process with Applications

2019/10/15 by Guanyu Hu, Hu, Guanyu, Fred Huffer +3 · 2 citations
Economics, Econometrics and Finance · Mathematics · #Applications (stat.AP) #Computation (stat.CO) #Economic and Environmental Valuation #FOS: Computer and information sciences #Point processes and geometric inequalities #Spatial and Panel Data Analysis

paper · pdf · doi:10.48550/arxiv.1910.06870

openalex publication_date 2019/10/15 · openalex created_date 2022/07/28 · openalex updated_date 2026/07/28

Abstract

Selecting important spatial-dependent variables under the nonhomogeneous\nspatial Poisson process model is an important topic of great current interest.\nIn this paper, we use the Deviance Information Criterion (DIC) and Logarithm of\nthe Pseudo Marginal Likelihood (LPML) for Bayesian variable selection under the\nnonhomogeneous spatial Poisson process model. We further derive the new Monte\nCarlo estimation formula for LPML in the spatial Poisson process setting.\nExtensive simulation studies are carried out to evaluate the empirical\nperformance of the proposed criteria. The proposed methodology is further\napplied to the analysis of two large data sets, the Earthquake Hazards Program\nof United States Geological Survey (USGS) earthquake data and the Forest of\nBarro Colorado Island (BCI) data.\n

Citations

• Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC

Cited by

• Spatial Weibull Regression with Multivariate Log Gamma Process and Its Applications to China Earthquake Economic Loss
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