On recursive estimation for time varying autoregressive processes

• 成果类型: Article
• 署名作者: Moulines, E; Priouret, P; Roueff, F
• 署名单位: IMT - Institut Mines-Telecom; Institut Polytechnique de Paris; Telecom Paris; Sorbonne Universite; Centre National de la Recherche Scientifique (CNRS); Sorbonne Universite
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/009053605000000624
• 发表日期: 2005
• 页码: 2610-2654
• 关键词: stochastic tracking algorithms; series models

摘要

This paper focuses on recursive estimation of time varying autoregressive processes in a nonparametric setting. The stability of the model is revisited and uniform results are provided when the time-varying autoregressive parameters belong to appropriate smoothness classes. An adequate normalization for the correction term used in the recursive estimation procedure allows for very mild assumptions on the innovations distributions. The rate of convergence of the pointwise estimates is shown to be minimax in beta-Lipschitz classes for 0 < beta <= 1. For 1 < beta <= 2, this property no longer holds. This can be seen by using an asymptotic expansion of the estimation error. A bias reduction method is then proposed for recovering the minimax rate.