A central limit theorem for random fields.
A maximum likelihood estimator of an inhomogeneous Poisson point processes intensity using beta splines
The problem of estimating the intensity of a non-stationary Poisson point process arises in many applications. Besides non parametric solutions, e. g. kernel estimators, parametric methods based on maximum likelihood estimation are of interest. In the present paper we have developed an approach in which the parametric function is represented by two-dimensional beta-splines.
A note on the properties of generalised separable spatial autoregressive process.
Analyse des redondances et régression PLS appliquées aux données spatiales. Comparaison avec l'estimation par krigeage et par inverse de la distance
Approximate maximum likelihood estimation for a spatial point pattern.
Several authors have proposed stochastic and non-stochastic approximations to the maximum likelihood estimate for a spatial point pattern. This approximation is necessary because of the difficulty of evaluating the normalizing constant. However, it appears to be neither a general theory which provides grounds for preferring a particular method, nor any extensive empirical comparisons. In this paper, we review five general methods based on approximations to the maximum likelihood estimate which have...
Asymptotic behavior of the likelihood function of covariance matrices of spatial Gaussian processes.