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April 2019 Estimation and prediction using generalized Wendland covariance functions under fixed domain asymptotics
Moreno Bevilacqua, Tarik Faouzi, Reinhard Furrer, Emilio Porcu
Ann. Statist. 47(2): 828-856 (April 2019). DOI: 10.1214/17-AOS1652

Abstract

We study estimation and prediction of Gaussian random fields with covariance models belonging to the generalized Wendland (GW) class, under fixed domain asymptotics. As for the Matérn case, this class allows for a continuous parameterization of smoothness of the underlying Gaussian random field, being additionally compactly supported. The paper is divided into three parts: first, we characterize the equivalence of two Gaussian measures with GW covariance function, and we provide sufficient conditions for the equivalence of two Gaussian measures with Matérn and GW covariance functions. In the second part, we establish strong consistency and asymptotic distribution of the maximum likelihood estimator of the microergodic parameter associated to GW covariance model, under fixed domain asymptotics. The third part elucidates the consequences of our results in terms of (misspecified) best linear unbiased predictor, under fixed domain asymptotics. Our findings are illustrated through a simulation study: the former compares the finite sample behavior of the maximum likelihood estimation of the microergodic parameter with the given asymptotic distribution. The latter compares the finite-sample behavior of the prediction and its associated mean square error when using two equivalent Gaussian measures with Matérn and GW covariance models, using covariance tapering as benchmark.

Citation

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Moreno Bevilacqua. Tarik Faouzi. Reinhard Furrer. Emilio Porcu. "Estimation and prediction using generalized Wendland covariance functions under fixed domain asymptotics." Ann. Statist. 47 (2) 828 - 856, April 2019. https://doi.org/10.1214/17-AOS1652

Information

Received: 1 December 2016; Revised: 1 August 2017; Published: April 2019
First available in Project Euclid: 11 January 2019

zbMATH: 07033153
MathSciNet: MR3909952
Digital Object Identifier: 10.1214/17-AOS1652

Subjects:
Primary: 62M30
Secondary: 60G25, 62F12

Rights: Copyright © 2019 Institute of Mathematical Statistics

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Vol.47 • No. 2 • April 2019
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