On the formal specification of sum-zero constrained intrinsic conditional autoregressive models

• Published in: Spatial statistics 2018-04, Vol.24, p.54-65
• Main Authors: Keefe, Matthew J., Ferreira, Marco A.R., Franck, Christopher T.
• Format: Article
• Language: English
• Subjects: Areal data; Conditional autoregressive; Intrinsic autoregressive; Model selection; Spatial statistics
• ISSN: 2211-6753; 2211-6753
• DOI: 10.1016/j.spasta.2018.03.007

We propose a formal specification for sum-zero constrained intrinsic conditional autoregressive (ICAR) models. Our specification first projects a vector of proper conditional autoregressive spatial random effects onto a subspace where the projected vector is constrained to sum to zero, and after that takes the limit when the proper conditional autoregressive model approaches the ICAR model. As a result, we show that the sum-zero constrained ICAR model has a singular Gaussian distribution with zero mean vector and a unique covariance matrix. Previously, sum-zero constraints have typically been imposed on the vector of spatial random effects in ICAR models within a Markov chain Monte Carlo (MCMC) algorithm in what is known as centering-on-the-fly. This mathematically informal way to impose the sum-zero constraint obscures the actual joint density of the spatial random effects. By contrast, the present work elucidates a unique distribution for ICAR random effects. The explicit expressions for the resulting unique covariance matrix and density function are useful for the development of Bayesian methodology in spatial statistics which will be useful to practitioners. We illustrate the practical relevance of our results by using Bayesian model selection to jointly assess both spatial dependence and fixed effects.