Parameter estimation for partially observed stochastic differential equations driven by fractional Brownian motion

被引：4

作者：

Wei, Chao ^{[1
]}

机构：

[1] Anyang Normal Univ, Sch Math & Stat, Anyang 455000, Peoples R China

来源：

AIMS MATHEMATICS | 2022年 / 7卷 / 07期

基金：

中国国家自然科学基金;

关键词：

parameter estimation; partially observed stochastic differential equations; fractional Brownian motion; strong consistency; asymptotic normality; ORNSTEIN-UHLENBECK PROCESS; ESTIMATION ALGORITHM; STATE ESTIMATION; NETWORKS; SYSTEMS; MODEL;

D O I：

10.3934/math.2022717

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

This paper is concerned with parameter estimation for partially observed stochastic differential equations driven by fractional Brownian motion. Firstly, the state estimation equation is given and the parameter estimator is derived. Then, the strong consistency and asymptotic normality of the maximum likelihood estimator are derived by applying the strong law of large numbers for continuous martingales and the central limit theorem for stochastic integrals with respect to Gaussian martingales. Finally, an example is provided to verify the results.

引用

页码：12952 / 12961

页数：10

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