This paper first strictly proved that the growth of the second moment of a large class of Gaussian processes is not greater than power function and the covariance matrix is strictly positive definite. Under these two conditions, the maximum likelihood estimators of the mean and variance of such classes of drift Gaussian process have strong consistency under broader growth of t_n. At the same time, the asymptotic normality of binary random vectors and the Berry-Ess\'{e}en bound of estimators are obtained by using the Stein's method via Malliavian calculus.

翻译：本文首先严格证明,大型高斯进程第二时刻的增长并不大于电力功能,而共变量矩阵是绝对肯定的。 在这两种条件下,此类漂移高斯进程平均值和差值的最大概率估计者在更大范围的 t_n 增长下具有很强的一致性。 同时,二进制随机矢量的无症状常态和通过马利亚维亚计算法使用斯坦因的方法获得估计值的贝里-埃斯/{e}约束。