Spatial Weibull Regression with Multivariate Log Gamma Process and Its Applications to China Earthquake Economic Loss
Spatial Weibull Regression with Multivariate Log Gamma Process and Its Applications to China Earthquake Economic Loss
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DOI：
10.4310/21-sii672
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发表时间：
2019-12
期刊：
影响因子：
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通讯作者：
Hou‐Cheng Yang;Lijiang Geng;Yishu Xue;Guanyu Hu
中科院分区：
文献类型：
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作者：
Hou‐Cheng Yang;Lijiang Geng;Yishu Xue;Guanyu Hu
近几十年来，重尾分布的贝叶斯空间建模在各个科学领域越来越受欢迎。本文提出了一个考虑空间随机效应的Weibull回归模型来分析极端经济损失。利用多元Log-Gamma分布的计算高效的贝叶斯抽样算法有助于模型估计。仿真研究表明，所提出的模型比广义线性混合效应模型更好的经验性能。分析了云南省地震局提供的一次地震资料。通过对拟边际似然值的对数进行选择，得到最优模型，并计算不同置信水平下最优模型的后验预测分布的风险值、期望损失和尾风险值。
Bayesian spatial modeling of heavy-tailed distributions has become increasingly popular in various areas of science in recent decades. We propose a Weibull regression model with spatial random effects for analyzing extreme economic loss. Model estimation is facilitated by a computationally efficient Bayesian sampling algorithm utilizing the multivariate Log-Gamma distribution. Simulation studies are carried out to demonstrate better empirical performances of the proposed model than the generalized linear mixed effects model. An earthquake data obtained from Yunnan Seismological Bureau, China is analyzed. Logarithm of the Pseudo-marginal likelihood values are obtained to select the optimal model, and Value-at-risk, expected shortfall, and tail-value-at-risk based on posterior predictive distribution of the optimal model are calculated under different confidence levels.