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Abstract:
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One of the main goals in spatial epidemiology is to study the geographical pattern of disease risks. For such purpose, the convolution model composed of correlated and uncorrelated components is often used. However, one of the two components could be predominant in some regions. To investigate the predominance of the correlated or uncorrelated component for multiple scale data, we propose four different spatial mixture multiscale models by using mixing spatially-varying probability weights of correlated and uncorrelated heterogeneity. The first model assumes that there is no linkage between the different scales. The second model introduces linkage between finer and coarser scales via a shared uncorrelated component of the mixture convolution model. The third model is similar to the second model but the linkage between the scales is introduced through the correlated component. Finally, the fourth model accommodates for a scale effect by sharing both the correlated and uncorrelated heterogeneity simultaneously. We applied these models to real and simulated data, and we found that the fourth model is the best model followed by the second model.
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