Asymptotic equivalence of nonparametric autoregression and nonparametric regression

• 成果类型: Article
• 署名作者: Grama, Ion G.; Neumann, Michael H.
• 署名单位: Friedrich Schiller University of Jena
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/009053606000000560
• 发表日期: 2006
• 页码: 1701-1732
• 关键词: gaussian white-noise; DENSITY-ESTIMATION; Minimax Risk; diffusion; approximation; drift

摘要

It is proved that nonparametric autoregression is asymptotically equivalent in the sense of Le Cam's deficiency distance to nonparametric regression with random design as well as with regular nonrandom design.