Time series with additive noise

• 成果类型: Article
• 署名作者: So, MKP
• 署名单位: Hong Kong University of Science & Technology
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/86.2.474
• 发表日期: 1999
• 页码: 474482
• 关键词: long-memory; monte-carlo; smoother

摘要

We put forward a state space model where the unobservable state variable can be any Gaussian stochastic process. We discuss both maximum likelihood estimation and Bayesian inference for this generalised model. The methodology developed in this paper is particularly important for the class of long memory plus noise models. Armed with the simulation smoother introduced in this paper, we can estimate a class of non-Gaussian measurement time series models with long memory in the state equation.