Hierarchical Multivariate CAR Models for Spatio-Temporally Correlated Survival Data

Abstract

Abstract The analysis of spatially referenced data has been an increasingly active area of both methodological and applied statistical research. As with many fields, advances in computing and software have been largely responsible. Modern database engines, often widely and publicly available to anyone with a web browser, provide an enormous supply of georeferenced data. Sophisticated computer programs known as geographic information systems (GISs) have revolutionized the analysis and display of such data sets, through their ability to “layer” multiple data sources over a common study area. Finally, Markov chain Monte Carlo (MCMC) algorithms enable the fitting of complex hierarchical models in a Bayesian framework, permitting full posterior inference for underlying parameters in even complex model settings (e.g., those involving data that are highly multivariate, temporally correlated, and perhaps even spatially or temporally misaligned).

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