• 论文 •

### 贝叶斯时空分位回归模型及其对北京市PM2.5浓度的研究

• 出版日期:2016-12-15 发布日期:2016-12-23

### Bayesian Spatio-temporal Quantile Regression Model and its Application for the Concentration of PM2.5 in Beijing

Mei Bo & Tian Maozai

• Online:2016-12-15 Published:2016-12-23

Abstract: Based on the spatio-temporal model and asymmetric Laplace distribution, this paper proposes a new spatio-temporal quantile regression model. In this article, the spatial fields are expanded by Thin Plate Regression Spline, combining with the relationship between the mixed model and splines, we derive the hierarchical Bayesian quantile regression model. Using MCMC algorithm, we get the posterior distributions of unknown parameters, then the predictions of spatial fields are carried out. Besides, computational complexity is alleviated by rank-reducing method. Different from the existed spatio-temporal quantile models, we model the relationship between response and covariates, rather than the whole effects of spatial or temporal terms, which is helpful to study the spatial structure between interested variable and others related. Simulation results show that the predictions of spatial fields is very close to their real values, additionally, under different quantiles, the model could effectively estimates the difference between quantile effects. Finally, we apply our model to the concentration of PM2.5 in Beijing, analyzing the spatially distributional characters of the meteorological variables’ effects on PM2.5.