Bandwidth plays an important role in determining the performance of local linear estimators. In this paper, we propose a Bayesian approach to bandwidth selection for local linear estimation of time-varying coefficient time series models, where the errors are assumed to follow the Gaussian kernel...

This study considers the estimation of spatial autoregressive models with censored dependent variables, where the spatial autocorrelation exists within the uncensored latent dependent variables. The estimator proposed in this paper is semiparametric, in the sense that the error distribution is...

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...