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.
Bayesian MCMC estimation of the rose of directions
The paper concerns estimation of the rose of directions of a stationary fibre process in from the intersection counts of the process with test planes. A new approach is suggested based on Bayesian statistical techniques. The method is derived from the special case of a Poisson line process however the estimator is shown to be consistent generally. Markov chain Monte Carlo (MCMC) algorithms are used for the approximation of the posterior distribution. Uniform ergodicity of the algorithms used is...
Bayesian methods in hydrology: a review.
Hydrology and water resources management are inherently affected by uncertainty in many of their involved processes, including inflows, rainfall, water demand, evaporation, etc. Statistics plays, therefore, an essential role in their study. We review here some recent advances within Bayesian statistics and decision analysis which will have a profound impact in these fields.
Boolean cluster models: mean cluster dilations and spherical contact distances
Boolean cluster point processes with various cluster distributions are examined by means of their spherical contact distribution function. Special attention is paid to clusters with strong independence properties (Poisson clusters) and regular clusters.
Concatenación temporal de modelos espaciales y su aplicación al estudio de la meningitis en España.
La cartografía de enfermedades infecciosas en periodos sucesivos plantea la necesidad de su extensión al caso dinámico. En este trabajo proponemos la concatenación temporal de modelos auto-regresivos espaciales para abordar el análisis de mortalidad por meningitis en España en el período 1950-1990 con datos agregados a nivel provincial. Para la estimación v selección del modelo usamos técnicas basadas en la función de verosimilitud.
Detección de rasgos en imágenes binarias mediante procesos puntuales espaciales marcados.
En este trabajo consideramos el problema de la detección de rasgos bajo la presencia de ruido en imágenes que tras un cierto tratamiento se reducen a binarias, por la presencia de dos tipos de elementos. Podemos encontrar ejemplos de este problema en la detección de minas por medio de imágenes de avión o satélite, en la búsqueda de rasgos en imágenes microscópicas de células, o en la caracterización de fallas en zonas de terremotos.En primer lugar revisamos algunos métodos de detección jerárquicos...
Development of the kriging method with application
This paper describes a modification of the kriging method for working with the square root transformation of a spatial random process. We have developed this method for the situation where the spatial process observed is not supposed to be stationary but the assumption is that its square root is a second order stationary spatial random process. Consequently this method is developed for estimating the integral of the process observed and finally some application of the method is given to data from...
Divergences of Gauss-Markov random fields with application to statistical inference
Estimating an even spherical measure from its sine transform
To reconstruct an even Borel measure on the unit sphere from finitely many values of its sine transform a least square estimator is proposed. Applying results by Gardner, Kiderlen and Milanfar we estimate its rate of convergence and prove strong consistency. We close this paper by giving an estimator for the directional distribution of certain three-dimensional stationary Poisson processes of convex cylinders which have applications in material science.
Estimation of summary characteristics from replicated spatial point processes
Summary characteristics play an important role in the analysis of spatial point processes. We discuss various approaches to estimating summary characteristics from replicated observations of a stationary point process. The estimators are compared with respect to their integrated squared error. Simulations for three basic types of point processes help to indicate the best way of pooling the subwindow estimators. The most appropriate way depends on the particular summary characteristic, edge-correction...
Estimation of the density of a determinantal process
We consider the problem of estimating the density of a determinantal process from the observation of independent copies of it. We use an aggregation procedure based on robust testing to build our estimator. We establish non-asymptotic risk bounds with respect to the Hellinger loss and deduce, when goes to infinity, uniform rates of convergence over classes of densities of interest.
Extreme values and kernel estimates of point processes boundaries
We present a method for estimating the edge of a two-dimensional bounded set, given a finite random set of points drawn from the interior. The estimator is based both on a Parzen-Rosenblatt kernel and extreme values of point processes. We give conditions for various kinds of convergence and asymptotic normality. We propose a method of reducing the negative bias and edge effects, illustrated by some simulations.
Extreme values and kernel estimates of point processes boundaries
We present a method for estimating the edge of a two-dimensional bounded set, given a finite random set of points drawn from the interior. The estimator is based both on a Parzen-Rosenblatt kernel and extreme values of point processes. We give conditions for various kinds of convergence and asymptotic normality. We propose a method of reducing the negative bias and edge effects, illustrated by some simulations.
Free area estimation in a dynamic germ-grain model with renewal dropping process.
Gaussian limits for random geometric measures.