This work describes a Gaussian Markov random field model that includes several previously proposed models, and studies properties of their maximum likelihood (ML) and restricted maximum likelihood (REML) estimators in a special case. Specifically, for models where a particular relation holds...

We consider the estimation of parametric models for stationary spatial or spatio-temporal data on a d-dimensional lattice, for d = 2. The achievement of asymptotic efficiency under Gaussianity, and asymptotic normality more generally, with standard convergence rate, faces two obstacles. One is...

We estimate carbon dioxide emissions for the 3401 electoral districts of the Republic of Ireland combining data from the Census, the Household Budget Survey, the National Accounts, Environmental Accounts, and the Labour Accounts. The source data is available for many countries, but we are not...

Over $1.7 billion has been spent on the Conservation Reserve Program (CRP) since 1985. The purpose of this study is to show that these expenditures have aided the environment. Rather than quantify changes in environmental variables, a spatial econometric model is used to test if CRP enrollments...

Conditionally autoregressive (CAR) models have been extensively used for the analysis of spatial data in diverse areas, such as demography, economy, epidemiology and geography, as models for both latent and observed variables. In the latter case, the most common inferential method has been...