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

页码：279 / 330

页数：52

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