Least squares estimation;
Ornstein-Uhlenbeck process;
small alpha-stable noises;
consistency;
asymptotic distribution;
discrete observation;
MAXIMUM-LIKELIHOOD-ESTIMATION;
D O I:
10.1080/03610926.2021.1986537
中图分类号:
O21 [概率论与数理统计];
C8 [统计学];
学科分类号:
020208 ;
070103 ;
0714 ;
摘要:
This article is concerned with the drift parameter estimation for Ornstein-Uhlenbeck process driven by small alpha-stable noises. The contrast function is given to obtain the least squares estimators and the error of estimation are obtained. The consistency, the rate of convergence and asymptotic distribution of estimators are derived when a small dispersion coefficient epsilon -> 0 and n ->infinity simultaneously. Some numerical calculus and simulations are made to verify the effectiveness of the estimators.
机构:
Anhui Normal Univ, Dept Math, Wuhu 241000, Peoples R ChinaAnhui Normal Univ, Dept Math, Wuhu 241000, Peoples R China
Shen, Guangjun
Yu, Qian
论文数: 0引用数: 0
h-index: 0
机构:
Anhui Normal Univ, Dept Math, Wuhu 241000, Peoples R China
East China Normal Univ, Sch Stat, Shanghai 200062, Peoples R ChinaAnhui Normal Univ, Dept Math, Wuhu 241000, Peoples R China
Yu, Qian
Tang, Zheng
论文数: 0引用数: 0
h-index: 0
机构:
Anhui Normal Univ, Dept Math, Wuhu 241000, Peoples R China
Chuzhou Univ, Sch Math & Finance, Chuzhou 239012, Peoples R ChinaAnhui Normal Univ, Dept Math, Wuhu 241000, Peoples R China
机构:
Kyushu Univ, Grad Sch Math, Nishi Ku, 744 Motooka, Fukuoka 8190395, Japan
Univ Sebelas Maret, Pendidikan Matemat FKIP, Jl Ir Sutami 36A, Surakarta 57126, IndonesiaKyushu Univ, Grad Sch Math, Nishi Ku, 744 Motooka, Fukuoka 8190395, Japan
Pramesti, Getut
MONTE CARLO METHODS AND APPLICATIONS,
2023,
29
(01):
: 1
-
32
机构:
Hangzhou City Univ, Hangzhou Yiyuan Technol Co Ltd, Inst Digital Finance, Hangzhou, BrazilHangzhou City Univ, Hangzhou Yiyuan Technol Co Ltd, Inst Digital Finance, Hangzhou, Brazil
Li, Yicun
Teng, Yuanyang
论文数: 0引用数: 0
h-index: 0
机构:
Zhejiang Univ, Hangzhou Yiyuan Technol Co Ltd, Hangzhou, Peoples R ChinaHangzhou City Univ, Hangzhou Yiyuan Technol Co Ltd, Inst Digital Finance, Hangzhou, Brazil