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2017 Maximum likelihood estimation for a bivariate Gaussian process under fixed domain asymptotics
Daira Velandia, François Bachoc, Moreno Bevilacqua, Xavier Gendre, Jean-Michel Loubes
Electron. J. Statist. 11(2): 2978-3007 (2017). DOI: 10.1214/17-EJS1298

Abstract

We consider maximum likelihood estimation with data from a bivariate Gaussian process with a separable exponential covariance model under fixed domain asymptotics. We first characterize the equivalence of Gaussian measures under this model. Then consistency and asymptotic normality for the maximum likelihood estimator of the microergodic parameters are established. A simulation study is presented in order to compare the finite sample behavior of the maximum likelihood estimator with the given asymptotic distribution.

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Daira Velandia. François Bachoc. Moreno Bevilacqua. Xavier Gendre. Jean-Michel Loubes. "Maximum likelihood estimation for a bivariate Gaussian process under fixed domain asymptotics." Electron. J. Statist. 11 (2) 2978 - 3007, 2017. https://doi.org/10.1214/17-EJS1298

Information

Received: 1 July 2016; Published: 2017
First available in Project Euclid: 11 August 2017

zbMATH: 06790051
MathSciNet: MR3694574
Digital Object Identifier: 10.1214/17-EJS1298

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Vol.11 • No. 2 • 2017
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