Penalized Pseudolikelihood Inference in Spatial Interaction Models with Covariates
نویسندگان
چکیده
Given spatially located observed random variables (x; z) = f(x i ; z i)g i , we propose a new method for nonparametric estimation of the potential functions of a Markov Random Field p(xjz), based on a roughness penalty approach. The new estimator maximises the penalized log-pseudolikelihood function and is a natural cubic spline. The calculations involved do not rely on Monte Carlo simulation. We suggest the use of B-splines to stabilise the numerical procedure. An application in Bayesian image reconstruction is described.
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