分数阶Ornstein–Uhlenbeck过程的参数估计

IF 0.3 Q4 STATISTICS & PROBABILITY Random Operators and Stochastic Equations Pub Date : 2022-05-31 DOI:10.1515/rose-2022-2079
Fatima-Ezzahra Farah
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引用次数: 1

摘要

考虑分数阶Ornstein-Uhlenbeck模型的参数估计问题,该模型由随机微分方程d _ X t=- θ _ X t _ d _t +d _ B_t H {dX_t{=-}\theta X_tdt{+}dB_t{^}H{, t≥0 }}t{\geq 0给出,}其中θ >0{\theta >0}是一个待估计的未知参数,B H{ B^{H}}是一个带有Hurst参数H∈(0,1){H\in(0,1)的分数阶布朗运动}。给出了θ的一个估计量,并研究了它的强相合性和渐近正态性。我们证明的主要工具是论文[1]。努尔丁,D. Nualart和G. Peccati,总变分的布鲁尔-梅奇定理:最小规则下的改进率,随机过程。中国科学:地球科学[j]。
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On parameter estimation of fractional Ornstein–Uhlenbeck process
Abstract We consider a problem of parameter estimation for the fractional Ornstein–Uhlenbeck model given by the stochastic differential equation d ⁢ X t = - θ ⁢ X t ⁢ d ⁢ t + d ⁢ B t H {dX_{t}=-\theta X_{t}dt+dB_{t}^{H}} , t ≥ 0 {t\geq 0} , where θ > 0 {\theta>0} is an unknown parameter to be estimated and B H {B^{H}} is a fractional Brownian motion with Hurst parameter H ∈ ( 0 , 1 ) {H\in(0,1)} . We provide an estimator for θ, and then we study its strong consistency and asymptotic normality. The main tool in our proofs is the paper [I. Nourdin, D. Nualart and G. Peccati, The Breuer–Major theorem in total variation: Improved rates under minimal regularity, Stochastic Process. Appl. 131 2021, 1–20].
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来源期刊
Random Operators and Stochastic Equations
Random Operators and Stochastic Equations STATISTICS & PROBABILITY-
CiteScore
0.60
自引率
25.00%
发文量
24
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