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

DAIRA LUZ VELANDIA MUÑOZ, François Bachoc, Moreno Bevilacqua, Xavier Gendre, Jean Michel Loubes

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)2978-3007
Number of pages30
JournalElectronic Journal of Statistics
Volume11
Issue number2
DOIs
StatePublished - 1 Jan 2017

Keywords

  • Bivariate exponential model
  • Equivalent Gaussian measures
  • Infill asymptotics
  • Microergodic parameters

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