Robust Bayesian model selection for autoregressive processes with additive outliers

• 成果类型: Article
• 署名作者: Le, ND; Raftery, AE; Martin, RD
• 署名单位: University of Washington; University of Washington Seattle
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.2307/2291388
• 发表日期: 1996
• 页码: 123-131
• 关键词: time-series; ORDER

摘要

Autoregressive (AR) models of order k are often used for forecasting and control of time series, as well as for the estimation of functionals such as the spectrum. Here we propose a method that consists of calculating the posterior probabilities of the competing AR(k) models in a way that is robust to outliers, and then obtaining the predictive distributions of quantities of interest, such as future observations and the spectrum, as a weighted average of the predictive distributions conditional on each model. This method is based on the idea of robust Bayes factors, calculated by replacing the likelihood for the nominal model by a robust likelihood It draws on and synthesizes several recent research advances, namely robust filtering and the Laplace method for integrals, modified to take account of the finite range of the parameters. The method performs well in simulation experiments and on real and artificial data. Software is available from StatLib.