Multi-dimensional multivariate Gaussian Markov random fields with application to image processing
Journal of Multivariate Analysis
Bayesian forecasting and dynamic models (2nd ed.)
Bayesian forecasting and dynamic models (2nd ed.)
Multivariate spatial regression models
Journal of Multivariate Analysis
Computational Statistics & Data Analysis
Parallel exact sampling and evaluation of Gaussian Markov random fields
Computational Statistics & Data Analysis
Bayesian reference analysis for Gaussian Markov random fields
Journal of Multivariate Analysis
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
Modelling the impact of socioeconomic structure on spatial health outcomes
Computational Statistics & Data Analysis
Preface: Second Special issue on Computational Econometrics
Computational Statistics & Data Analysis
Generalized structured additive regression based on Bayesian P-splines
Computational Statistics & Data Analysis
A model for non-parametric spatially varying regression effects
Computational Statistics & Data Analysis
Efficient parallelisation of Metropolis-Hastings algorithms using a prefetching approach
Computational Statistics & Data Analysis
Multilevel structured additive regression
Statistics and Computing
Hi-index | 0.00 |
Space-varying regression models are generalizations of standard linear models where the regression coefficients are allowed to change in space. The spatial structure is specified by a multivariate extension of pairwise difference priors, thus enabling incorporation of neighboring structures and easy sampling schemes. Bayesian inference is performed by incorporation of a prior distribution for the hyperparameters. This approach leads to an untractable posterior distribution. Inference is approximated by drawing samples from the posterior distribution. Different sampling schemes are available and may be used in an MCMC algorithm. They basically differ in the way they handle blocks of regression coefficients. Approaches vary from sampling each location-specific vector of coefficients to complete elimination of all regression coefficients by analytical integration. These schemes are compared in terms of their computation, chain auto-correlation, and resulting inference. Results are illustrated with simulated data and applied to a real dataset. Related prior specifications that can accommodate the spatial structure in different forms are also discussed. The paper concludes with a few general remarks.