Data collected from spatial locations are often multivariate. Gaussian conditional autore-gressive (CAR) models, also known as Gaussian Markov random fields, are frequently used to analyze such continuous data, or as models for the parameters of discrete distributions. Two difficulties in Gaussian maximum likelihood estimation are ensuring that the parameter estimates are allowable values, and computing the likelihood efficiently. It is shown here that, for some commonly-used multivariate CAR models, checking for allowable parameter values can be facilitated, and the likelihood can be computed very quickly. (C) 2018 Elsevier Inc. All rights reserved.
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1016/j.jmva.2018.07.007|
|Codice identificativo ISI:||WOS:000447960800012|
|Codice identificativo Scopus:||2-s2.0-85050926954|
|Titolo:||Efficient likelihood computations for some multivariate Gaussian Markov random fields|
|Appare nelle tipologie:||1.1 Articolo in rivista|