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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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.

Article information

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

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

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1502416821

Digital Object Identifier
doi:10.1214/17-EJS1298

Zentralblatt MATH identifier
06790051

Keywords
Bivariate exponential model equivalent Gaussian measures infill asymptotics microergodic parameters

Rights
Creative Commons Attribution 4.0 International License.

Citation

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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