Maximum likelihood estimation for a bivariate Gaussian process under fixed domain asymptotics
Statistics Theory
2018-07-25 v1 Statistics Theory
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.
Cite
@article{arxiv.1603.09059,
title = {Maximum likelihood estimation for a bivariate Gaussian process under fixed domain asymptotics},
author = {Daira Velandia and François Bachoc and Moreno Bevilacqua and Xavier Gendre and Jean-Michel Loubes},
journal= {arXiv preprint arXiv:1603.09059},
year = {2018}
}