论文标题
Vasicek型号估计器的浆果上限
The Berry-Esséen Upper Bounds of Vasicek Model Estimators
论文作者
论文摘要
研究了MONM估计器的上限和最小二乘估计器的均值和漂移系数在Vasicek模型中由General Gaussian过程驱动的。在研究由分数布朗运动驱动的Ornstein-Uhlenbeck（OU）过程的参数估计问题时，通常使用的方法主要由Kim和Park给出，它们显示了Kolmogorov距离的上限是Kolmogorov距离的上限，而Kolmogorov的上限是两个双重Wiener-Itô-Itô-Itô积分的比率之间的分布和正常分布。本文的主要创新是扩展上述比率过程，也就是说，分子和分母分别最多包含三重Wiener-ItôStochastic积分。据我们所知，上述估计器和正态分布的分布之间的上限是新颖的。
The Berry-Esséen upper bounds of moment estimators and least squares estimators of the mean and drift coefficients in Vasicek models driven by general Gaussian processes are studied. When studying the parameter estimation problem of Ornstein-Uhlenbeck (OU) process driven by fractional Brownian motion, the commonly used methods are mainly given by Kim and Park, they show the upper bound of Kolmogorov distance between the distribution of the ratio of two double Wiener-Itô stochastic integrals and the Normal distribution. The main innovation in this paper is extending the above ratio process, that is to say, the numerator and denominator respectively contain triple Wiener-Itô stochastic integrals at most. As far as we know, the upper bounds between the distribution of above estimators and the Normal distribution are novel.
