Spatio-temporal modeling and prediction of CO concentrations in Tehran city

摘要

One of the most important agents responsible for high pollution in Tehran is carbon monoxide. Prediction of carbon monoxide is of immense help for sustaining the inhabitants' health level. In this paper, motivated by the statistical analysis of carbon monoxide using the empirical Bayes approach, we deal with the issue of prior specification for the model parameters. In fact, the hyperparameters (the parameters of the prior law) are estimated based on a sampling-based method which depends only on the specification of the marginal spatial and temporal correlation structures. We compare the predictive performance of this approach with the type II maximum likelihood method. Results indicate that the proposed procedure performs better for this data set.%Department of Statistics, Tarbiat Modares University, Tehran, Iran Department of Statistics,Shahid Beheshti University, Tehran, Iran;Department of Statistics, Tarbiat Modares University, Tehran, Iran; Department of Statistics,Shahid Beheshti University, Tehran, Iran;