@article{song_fuentes_ghosh_2008, title={A comparative study of Gaussian geostatistical models and Gaussian Markov random field models}, volume={99}, ISSN={["0047-259X"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-48549087068&partnerID=MN8TOARS}, DOI={10.1016/j.jmva.2008.01.012}, abstractNote={Gaussian geostatistical models (GGMs) and Gaussian Markov random fields (GMRFs) are two distinct approaches commonly used in spatial models for modeling point-referenced and areal data, respectively. In this paper, the relations between GGMs and GMRFs are explored based on approximations of GMRFs by GGMs, and approximations of GGMs by GMRFs. Two new metrics of approximation are proposed : (i) the Kullback–Leibler discrepancy of spectral densities and (ii) the chi-squared distance between spectral densities. The distances between the spectral density functions of GGMs and GMRFs measured by these metrics are minimized to obtain the approximations of GGMs and GMRFs. The proposed methodologies are validated through several empirical studies. We compare the performance of our approach to other methods based on covariance functions, in terms of the average mean squared prediction error and also the computational time. A spatial analysis of a dataset on PM2.5 collected in California is presented to illustrate the proposed method.}, number={8}, journal={JOURNAL OF MULTIVARIATE ANALYSIS}, author={Song, Hae-Ryoung and Fuentes, Montserrat and Ghosh, Sujit}, year={2008}, month={Sep}, pages={1681–1697} }
 @article{fuentes_song_ghosh_holland_davis_2006, title={Spatial association between speciated fine particles and mortality}, volume={62}, ISSN={["1541-0420"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-33748768427&partnerID=MN8TOARS}, DOI={10.1111/j.1541-0420.2006.00526.x}, abstractNote={Summary Particulate matter (PM) has been linked to a range of serious cardiovascular and respiratory health problems, including premature mortality. The main objective of our research is to quantify uncertainties about the impacts of fine PM exposure on mortality. We develop a multivariate spatial regression model for the estimation of the risk of mortality associated with fine PM and its components across all counties in the conterminous United States. We characterize different sources of uncertainty in the data and model the spatial structure of the mortality data and the speciated fine PM. We consider a flexible Bayesian hierarchical model for a space‐time series of counts (mortality) by constructing a likelihood‐based version of a generalized Poisson regression model that combines methods for point‐level misaligned data and change of support regression. Our results seem to suggest an increase by a factor of two in the risk of mortality due to fine particles with respect to coarse particles. Our study also shows that in the Western United States, the nitrate and crustal components of the speciated fine PM seem to have more impact on mortality than the other components. On the other hand, in the Eastern United States, sulfate and ammonium explain most of the fine PM effect.}, number={3}, journal={BIOMETRICS}, author={Fuentes, Montserrat and Song, Hae-Ryoung and Ghosh, Sujit K. and Holland, David M. and Davis, Jerry M.}, year={2006}, month={Sep}, pages={855–863} }