标题： 固定域渐近下的双变量高斯过程的最大似然估计 显示英文标题

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

摘要： 我们考虑在固定域渐近条件下，从具有可分离指数协方差模型的双变量高斯过程数据中进行最大似然估计。 我们首先在此模型下表征高斯测度的等价性。 然后建立了微遍历参数的一致性和渐近分布。 为了比较最大似然估计量的有限样本行为与给定的渐近分布，进行了模拟研究。 显示英文摘要

摘要： 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.