论文标题
在离散观察时漂移的高斯过程的参数估计
Parameter estimations for the Gaussian process with drift at discrete observation
论文作者
论文摘要
本文首先严格证明，大型高斯过程的第二刻的增长不大于功率函数，因此协方差矩阵严格确定。在这两种情况下，在T_N的更广泛的增长下，这种漂移高斯过程的平均值和方差的最大似然估计值具有很强的一致性。同时，通过通过malliavian conculus使用Stein的方法获得二进制随机载体和估计剂浆果界的渐近正态性。
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-Esséen bound of estimators are obtained by using the Stein's method via Malliavian calculus.
