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Shared Components Models in Joint Disease Mapping: A Comparison

  • Emanuela Dreassi
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

Two models for jointly analysing the spatial variation of incidences of three (or more) diseases, with common and uncommon risk factors, are compared via a simulation experiment. In both models, the linear predictor can be decomposed into shared and disease-specific spatial variability components. The two models are the shared model on the original formulation that use exchangeable Poisson distribution as response multivariate variable and shared components model that use a Multinomial one. The simulation study, performed using three different degree of spatial unstructured poisson over-dispersion, shows that models behave similarly. However, they perform differently for the shared clustering terms when a different level of spatial unstructured over-dispersion is present.

Keywords

Root Mean Square Error Multinomial Model Spatial Term Shared Component Gaussian Markov Random Field 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Statistic “G. Parenti”University of FlorenceFlorenceItaly

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