Statistics Seminar（2014-19）

Topic:Dimension Reduction and Alleviation of Confounding for Spatial Generalized Linear Mixed Models

Speaker:Murali Haran, Penn State University

Time:Monday, 22 December, 14:00-15:00

Location:Room 217, Guanghua Building 2

Abstract:Non-gaussian spatial data are very common in many disciplines. For instance, count data are common in disease mapping, and binary data are common in ecology. When fitting spatial regressions for such data, one needs to account for dependence to ensure reliable inference for the regression coefficients. The spatial generalized linear mixed model (SGLMM) offers a very popular and flexible approach for modeling such data, but the SGLMM suffers from three major shortcomings: (1) high-dimensional spatial random effects that make fully Bayesian inference for such models computationally challenging, (2) uninterpretability of parameters due to confounding between regression coefficients and spatial random effects. We propose a new parameterization of the SGLMM that alleviates spatial confounding and speeds computation by greatly reducing the dimension of the spatial random effects. We illustrate the application of our approach to simulated binary, count, and Gaussian spatial datasets, and to a large infant mortality dataset. This is joint work with John Hughes (University of Minnesota Biostatistics).

An earlier version of the paper (now published in JRSS(B)) //arxiv.org/abs/1011.6649