Given spatially located observed random variables (x, z = {(xi, zi)}i, we propose a new method for non-parametric estimation of the potential functions of a Markov random field p(x|z), based on a roughness penalty approach. The new estimator maximizes 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 stabilize the numerical procedure. An application in Bayesian image reconstruction is described.

Penalized Pseudolikelihood Inference in Spatial Interaction Models with Covariates

DIVINO, Fabio;
2000-01-01

Abstract

Given spatially located observed random variables (x, z = {(xi, zi)}i, we propose a new method for non-parametric estimation of the potential functions of a Markov random field p(x|z), based on a roughness penalty approach. The new estimator maximizes 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 stabilize the numerical procedure. An application in Bayesian image reconstruction is described.
http://onlinelibrary.wiley.com/doi/10.1111/1467-9469.00200/abstract
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11695/7367
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