Dimension Reduction and Alleviation of Confounding for Spatial Generalized Linear Mixed Models
空间广义线性混合模型的降维与空间混杂缓解
Summary 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 offers a very popular and flexible approach to modelling such data, but this model suffers from two major shortcomings:…