Second-order continuous-time non-stationary Gaussian autoregression

被引：0
|
作者：
Lin N. [1 ]
Lototsky S.V. [1 ]
机构：
[1] Department of Mathematics, USC, Los Angeles, CA
来源：
Statistical Inference for Stochastic Processes | 2014年 / 17卷 / 1期
基金：
美国国家科学基金会;
关键词：
Asymptotic mixed normality; Lyapunov exponent; Maximum likelihood estimation; Non-normal limit distribution; Rate of convergence; Second-order stochastic equation;
D O I：
10.1007/s11203-014-9090-9
中图分类号：
学科分类号：
摘要：
The objective of the paper is to identify and investigate all possible types of asymptotic behavior for the maximum likelihood estimators of the unknown parameters in the second-order linear stochastic ordinary differential equation driven by Gaussian white noise. The emphasis is on the non-ergodic case, when the roots of the corresponding characteristic equation are not both in the left half-plane. © 2014 Springer Science+Business Media Dordrecht.
引用
收藏
页码：19 / 49
页数：30
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