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

Abstract : We consider maximum likelihood estimation with data from a bivariate Gaussian process with a separable exponential covariance model under fixed domain asymptotic. We first characterize the equivalence of Gaussian measures under this model. Then consistency and asymptotic distribution for 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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Submitted on : Tuesday, March 29, 2016 - 2:16:29 PM
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  • HAL Id : hal-01294547, version 1
  • ARXIV : 1603.09059

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Velandia Daira, Bachoc François, Bevilacqua Moreno, Gendre Xavier, Jean-Michel Loubes. Maximum likelihood estimation for a bivariate Gaussian process under fixed domain asymptotics. 2016. ⟨hal-01294547⟩

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