在高斯移动平均过程是半鞅情况下,研究由其驱动的随机微分方程的极大似然估计获得了渐近一致的估计,并证明了该估计量的强相容性和渐近正态性,从而推广了经典情形下的相应结果.
In this paper, we studied the maximum likelihood estimation (MLE) for a class of stochastic differen- tial equations driven by a Gaussian moving average process which was a semimartingale. Uniformly asymptotic estimation was obtained. Moreover, we proved the strong consistency and the asymptotic ,normality of the estima- tor, which generalizes the related results in the classical situation.