Abstract: When the spatial sample size is extremely large which occurs in many environmental and ecological studies, operations on the large covariance matrix are a numerical challenge. Covariance tapering is a technique to alleviate the numerical challenges. Under the assumption that data are collected along a line in a bounded region, we investigate how the tapering affects the asymptotic efficiency of the maximum likelihood estimator (MLE) for the microergodic parameter in the Matérn covariance function by establishing the fixed-domain asymptotic distribution of the exact MLE and that of the tapered MLE. Our results imply that under some conditions on the taper, the tapered MLE is asymptotically as efficient as the true MLE for the micr...
Consistency is shown for the minimum covariance determinant (MCD) estimators of multivariate locatio...
The quasi-maximum likelihood estimator for the autoregressive parameter in a spatial autoregression...
Spatial process models popular in geostatistics often represent the observed data as the sum of a sm...
Maximum likelihood is an attractive method of estimating covariance parameters in spatial models bas...
Two asymptotic frameworks, increasing domain asymptotics and infill asymptotics, have been advanced ...
Given a zero-mean Gaussian random field with a covariance function that belongs to a parametric fami...
Parameter estimation for and prediction of spatially or spatio-temporally correlated random processe...
Gaussian process models typically contain finite dimensional parameters in the covariance function t...
Given a zero-mean Gaussian random field with a covariance function that belongs to a parametric fami...
We study estimation and prediction of Gaussian random fields with covariance models belonging to the...
The best linear unbiased predictor (BLUP) is called a kriging predictor and has been widely used to ...
A common problem in spatial statistics is to predict a random field f at some spatial location t(0) ...
In the analysis of spatial data, the inverse of the covariance matrix needs to be calculated. For ex...
We consider maximum likelihood estimation with data from a bivariate Gaussian process with a separab...
In recent literature there has been a growing interest in the construction of covariance models for ...
Consistency is shown for the minimum covariance determinant (MCD) estimators of multivariate locatio...
The quasi-maximum likelihood estimator for the autoregressive parameter in a spatial autoregression...
Spatial process models popular in geostatistics often represent the observed data as the sum of a sm...
Maximum likelihood is an attractive method of estimating covariance parameters in spatial models bas...
Two asymptotic frameworks, increasing domain asymptotics and infill asymptotics, have been advanced ...
Given a zero-mean Gaussian random field with a covariance function that belongs to a parametric fami...
Parameter estimation for and prediction of spatially or spatio-temporally correlated random processe...
Gaussian process models typically contain finite dimensional parameters in the covariance function t...
Given a zero-mean Gaussian random field with a covariance function that belongs to a parametric fami...
We study estimation and prediction of Gaussian random fields with covariance models belonging to the...
The best linear unbiased predictor (BLUP) is called a kriging predictor and has been widely used to ...
A common problem in spatial statistics is to predict a random field f at some spatial location t(0) ...
In the analysis of spatial data, the inverse of the covariance matrix needs to be calculated. For ex...
We consider maximum likelihood estimation with data from a bivariate Gaussian process with a separab...
In recent literature there has been a growing interest in the construction of covariance models for ...
Consistency is shown for the minimum covariance determinant (MCD) estimators of multivariate locatio...
The quasi-maximum likelihood estimator for the autoregressive parameter in a spatial autoregression...
Spatial process models popular in geostatistics often represent the observed data as the sum of a sm...