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http://dx.doi.org/10.5351/KJAS.2015.28.2.353

Review of Spatial Linear Mixed Models for Non-Gaussian Outcomes  

Park, Jincheol (Department of Statistics, Keimyung University)
Publication Information
The Korean Journal of Applied Statistics / v.28, no.2, 2015 , pp. 353-360 More about this Journal
Abstract
Various statistical models have been proposed over the last decade for spatially correlated Gaussian outcomes. The spatial linear mixed model (SLMM), which incorporates a spatial effect as a random component to the linear model, is the one of the most widely used approaches in various application contexts. Employing link functions, SLMM can be naturally extended to spatial generalized linear mixed model for non-Gaussian outcomes (SGLMM). We review popular SGLMMs on non-Gaussian spatial outcomes and demonstrate their applications with available public data.
Keywords
Non-Gaussian data; spatial generalized linear mixed model;
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