Parameter estimation for ergodic linear SDEs from partial and discrete observations

被引：2
|
作者：
Kurisaki, Masahiro [1 ]
机构：
[1] Univ Tokyo, Grad Sch Math Sci, Tokyo, Japan
来源：
STATISTICAL INFERENCE FOR STOCHASTIC PROCESSES | 2023年 / 26卷 / 02期
关键词：
Partially observed linear model; State space model; Hidden Ornstein Uhlenbeck model; Kalman-Bucy filter; Quasi-likelihood analysis;
D O I：
10.1007/s11203-023-09288-w
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：
We consider a problem of parameter estimation for the state space model described by linear stochastic differential equations. We assume that an unobservable Ornstein-Uhlenbeck process drives another observable process by the linear stochastic differential equation, and these two processes depend on some unknown parameters. We construct the quasi-maximum likelihood estimator of the unknown parameters and show asymptotic properties of the estimator.
引用
收藏
页码：279 / 330
页数：52
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