Electronic Journal of Statistics

Maximum likelihood estimation for a bivariate Gaussian process under fixed domain asymptotics

Daira Velandia, François Bachoc, Moreno Bevilacqua, Xavier Gendre, and Jean-Michel Loubes

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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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Electron. J. Statist. Volume 11, Number 2 (2017), 2978-3007.

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

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Bivariate exponential model equivalent Gaussian measures infill asymptotics microergodic parameters

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Velandia, Daira; Bachoc, François; Bevilacqua, Moreno; Gendre, Xavier; Loubes, Jean-Michel. Maximum likelihood estimation for a bivariate Gaussian process under fixed domain asymptotics. Electron. J. Statist. 11 (2017), no. 2, 2978--3007. doi:10.1214/17-EJS1298. https://projecteuclid.org/euclid.ejs/1502416821

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