Likelihood analysis of non-Gaussian measurement time series

• 成果类型: Article
• 署名作者: Shephard, N; Pitt, MK
• 署名单位: University of Oxford
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/84.3.653
• 发表日期: 1997
• 页码: 653667
• 关键词: state-space models; generalized linear-models; monte-carlo; smoother; sampler

摘要

In this paper we provide methods for estimating non-Gaussian time series models. These techniques rely on Markov chain Monte Carlo to carry out simulation smoothing and Bayesian posterior analysis of parameters, and on importance sampling to estimate the likelihood function for classical inference. The time series structure of the models is used to ensure that our simulation algorithms are efficient.