Numerical simulations recently reported in the literature have shown that the profile likelihood associated with the estimation of covariance range parameters for a Gaussian field can be multimodal. Here we complement these recent results by considering covari-ances with unknown nugget, scale and range parameters. Estimation is performed in a restricted maximum likelihood framework. For unbounded sampling domains and known range parameter, conditions ensuring asymptotic unimodality of the restricted likelihood are derived. For bounded sampling domains, Monte Carlo simulations indicate that if the range parameter is known, multimodality is relatively rare unless the sample size is small. For unknown range parameter, frequencies of multimodal...
The paper addresses the multimodal sensor selection problem where selected colocated sensor nodes ar...
The 12th International Conference on Computational and Financial Econometrics (CFE 2018) and the 11t...
The covariance structure of spatial Gaussian predictors (aka Kriging predictors) is generally model...
The limitations of the maximum likelihood method for estimating spatial covariance parameters are: t...
Two asymptotic frameworks, increasing domain asymptotics and infill asymptotics, have been advanced ...
Composite likelihood methods have become popular in spatial statistics. This is mainly due to the fa...
We provide a computationally and statistically efficient method for estimating the parameters of a s...
Maximum likelihood is an attractive method of estimating covariance parameters in spatial models bas...
Given a set of spatial data, often the desire is to estimate its covariance structure. For prac-tica...
Spatial models have been widely used in the public health set-up. In the case of continuous outcomes...
A simulation study is implemented to study estimators of the covariance structure of a stationary Ga...
In recent literature there has been a growing interest in the construction of covariance models for ...
This article is motivated by the difficulty of applying standard simulation techniques when identifi...
Covariance parameter estimation of Gaussian processes is analyzed in an asymptotic frame-work. The s...
This article is motivated by the difficulty of applying standard simulation techniques when iden-tif...
The paper addresses the multimodal sensor selection problem where selected colocated sensor nodes ar...
The 12th International Conference on Computational and Financial Econometrics (CFE 2018) and the 11t...
The covariance structure of spatial Gaussian predictors (aka Kriging predictors) is generally model...
The limitations of the maximum likelihood method for estimating spatial covariance parameters are: t...
Two asymptotic frameworks, increasing domain asymptotics and infill asymptotics, have been advanced ...
Composite likelihood methods have become popular in spatial statistics. This is mainly due to the fa...
We provide a computationally and statistically efficient method for estimating the parameters of a s...
Maximum likelihood is an attractive method of estimating covariance parameters in spatial models bas...
Given a set of spatial data, often the desire is to estimate its covariance structure. For prac-tica...
Spatial models have been widely used in the public health set-up. In the case of continuous outcomes...
A simulation study is implemented to study estimators of the covariance structure of a stationary Ga...
In recent literature there has been a growing interest in the construction of covariance models for ...
This article is motivated by the difficulty of applying standard simulation techniques when identifi...
Covariance parameter estimation of Gaussian processes is analyzed in an asymptotic frame-work. The s...
This article is motivated by the difficulty of applying standard simulation techniques when iden-tif...
The paper addresses the multimodal sensor selection problem where selected colocated sensor nodes ar...
The 12th International Conference on Computational and Financial Econometrics (CFE 2018) and the 11t...
The covariance structure of spatial Gaussian predictors (aka Kriging predictors) is generally model...