GENERALIZED LINEAR MIXED MODELS WITH SPATIAL RANDOM EFFECTS FOR SPATIO-TEMPORAL DATA: AN APPLICATION TO DENGUE FEVER MAPPING
- 1 Kasetsart University, Thailand
Abstract
The Generalized Linear Mixed Models (GLMMs) with spatial random effects for spatio-temporal data are proposed. A hierarchical Bayesian method is used for parameter estimation. The random effects are assumed to be normally distributed and the spatial random effects are assumed to be proper Conditional Autoregressive (CAR) models. The proposed models are applied to Dengue fever data in Northern Thailand, including climatic covariates, rainfall and temperature. The Dengue fever maps are constructed from the posterior mean of the mortality rates.
DOI: https://doi.org/10.3844/jmssp.2013.137.143
Copyright: © 2013 Krisada Lekdee and Lily Ingsrisawang. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Keywords
- Generalized Linear Mixed Models
- Conditional Autoregressive Models
- Spatial Random Effects Spatio-Temporal Data
- Dengue Fever Maps