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 the "edge effect", which worsens with increasing d. The other is the difficulty of computing a continuous-frequency form of Whittle estimate or a time domain Gaussian maximum likelihood estimate, especially in case of multilateral models, due mainly to the Jacobian term. An extension of the discrete-frequency Whittle estimate from the time series literature deals conveniently with the latter problem, but when subjected to a standard device f...
We consider the estimation of the parameters of a stationary random field on d-dimensional lattice b...
The local Whittle (or Gaussian semiparametric) estimator of long range depen-dence, proposed by Küns...
We develop new higher-order asymptotic techniques for the Gaussian maximum likelihood estimator of t...
In the estimation of parametric models for stationary spatial or spatio-temporal data on a d-dimensi...
AbstractIn the estimation of parametric models for stationary spatial or spatio-temporal data on a d...
In the estimation of parametric models for stationary spatial or spatio-temporal data on a d-dimensi...
In the estimation of parametric models for stationary spatial or spatio-temporal data on a d-dimensi...
Following the ideas presented in Dahlhaus (2000) and Dahlhaus and Sahm (2000) for time series, we bu...
We provide a computationally and statistically efficient method for estimating the parameters of a s...
Maximum likelihood estimation of a spatial model typically requires a sizeable computational capacit...
We provide a computationally and statistically efficient method for estimating the parameters of a s...
Moving from univariate to bivariate jointly dependent long-memory time series introduces a phase par...
In many areas of the agriculture, biological, physical and social sciences, spatial lattice data are...
Two asymptotic frameworks, increasing domain asymptotics and infill asymptotics, have been advanced ...
The local Whittle (or Gaussian semiparametric) estimator of long range depen-dence, proposed by Küns...
We consider the estimation of the parameters of a stationary random field on d-dimensional lattice b...
The local Whittle (or Gaussian semiparametric) estimator of long range depen-dence, proposed by Küns...
We develop new higher-order asymptotic techniques for the Gaussian maximum likelihood estimator of t...
In the estimation of parametric models for stationary spatial or spatio-temporal data on a d-dimensi...
AbstractIn the estimation of parametric models for stationary spatial or spatio-temporal data on a d...
In the estimation of parametric models for stationary spatial or spatio-temporal data on a d-dimensi...
In the estimation of parametric models for stationary spatial or spatio-temporal data on a d-dimensi...
Following the ideas presented in Dahlhaus (2000) and Dahlhaus and Sahm (2000) for time series, we bu...
We provide a computationally and statistically efficient method for estimating the parameters of a s...
Maximum likelihood estimation of a spatial model typically requires a sizeable computational capacit...
We provide a computationally and statistically efficient method for estimating the parameters of a s...
Moving from univariate to bivariate jointly dependent long-memory time series introduces a phase par...
In many areas of the agriculture, biological, physical and social sciences, spatial lattice data are...
Two asymptotic frameworks, increasing domain asymptotics and infill asymptotics, have been advanced ...
The local Whittle (or Gaussian semiparametric) estimator of long range depen-dence, proposed by Küns...
We consider the estimation of the parameters of a stationary random field on d-dimensional lattice b...
The local Whittle (or Gaussian semiparametric) estimator of long range depen-dence, proposed by Küns...
We develop new higher-order asymptotic techniques for the Gaussian maximum likelihood estimator of t...