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...

Spatial generalized linear mixed models are common in applied statistics. Most users are satisfied using a Gaussian distribution for the spatial latent variables in this model, but it is unclear whether the Gaussian assumption holds. Wrong Gaussian assumptions cause bias in the parameter...

Non-Gaussian spatial data are common in many sciences such as environmental sciences, biology and epidemiology. Spatial generalized linear mixed models (SGLMMs) are flexible models for modeling these types of data. Maximum likelihood estimation in SGLMMs is usually made cumbersome due to the...

Gneiting (2002) proposed a nonseparable covariance model for spatial-temporal data. In the present paper we show that in certain circumstances his model possesses a counterintuitive dimple. In some cases, the magnitude of the dimple can be nontrivial. Copyright 2011, Oxford University Press.