Intrinsic Gaussian random fields generated by conditional autoregressive models are considered. A spectral approximation to the log-likelihood function of an intrinsic random field was given by Künsch. Here a rigorous treatment is given of the comparison between the exact and spectral log-likelihood functions as the domain of observations increases.intrinsic random fields spectral approximation conditional autoregression Whittle approximation trace class norm
In this article we discuss a generalization of the Whittle likelihood approximation from stationary ...
This paper presents an algorithm for simulating Gaussian random fields with zero mean and non-statio...
In this article we discuss a generalization of the Whittle likelihood approximation from stationary ...
AbstractIntrinsic Gaussian random fields generated by conditional autoregressive models are consider...
AbstractIntrinsic Gaussian random fields generated by conditional autoregressive models are consider...
In this paper we discuss how a Gaussian random field with Matérn covariance function can repre...
In this paper we discuss how a Gaussian random field with Matérn covariance function can repre...
In this paper we discuss how a Gaussian random field with Matérn covariance function can repre...
In this paper we discuss how a Gaussian random field with Matérn covariance function can repre...
In this paper we discuss how a Gaussian random field with Matérn covariance function can repre...
In this paper we discuss how a Gaussian random field with Matérn covariance function can repre...
Problems of uncertainty quantification usually involve large number realiza-tions of a stationary sp...
Gaussian random fields defined over compact two-point homogeneous spaces are considered and Sobolev ...
Following the ideas presented in Dahlhaus (2000) and Dahlhaus and Sahm (2000) for time series, we bu...
This article presents a variant of the spectral turning bands method that allows fast and accurate s...
In this article we discuss a generalization of the Whittle likelihood approximation from stationary ...
This paper presents an algorithm for simulating Gaussian random fields with zero mean and non-statio...
In this article we discuss a generalization of the Whittle likelihood approximation from stationary ...
AbstractIntrinsic Gaussian random fields generated by conditional autoregressive models are consider...
AbstractIntrinsic Gaussian random fields generated by conditional autoregressive models are consider...
In this paper we discuss how a Gaussian random field with Matérn covariance function can repre...
In this paper we discuss how a Gaussian random field with Matérn covariance function can repre...
In this paper we discuss how a Gaussian random field with Matérn covariance function can repre...
In this paper we discuss how a Gaussian random field with Matérn covariance function can repre...
In this paper we discuss how a Gaussian random field with Matérn covariance function can repre...
In this paper we discuss how a Gaussian random field with Matérn covariance function can repre...
Problems of uncertainty quantification usually involve large number realiza-tions of a stationary sp...
Gaussian random fields defined over compact two-point homogeneous spaces are considered and Sobolev ...
Following the ideas presented in Dahlhaus (2000) and Dahlhaus and Sahm (2000) for time series, we bu...
This article presents a variant of the spectral turning bands method that allows fast and accurate s...
In this article we discuss a generalization of the Whittle likelihood approximation from stationary ...
This paper presents an algorithm for simulating Gaussian random fields with zero mean and non-statio...
In this article we discuss a generalization of the Whittle likelihood approximation from stationary ...