Spline-backfitted kernel smoothing of nonlinear additive autoregression model

• 成果类型: Article
• 署名作者: Wang, Li; Yang, Lijian
• 署名单位: University System of Georgia; University of Georgia; Michigan State University
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/009053607000000488
• 发表日期: 2007
• 页码: 2474-2503
• 关键词: regression-models; nonparametric-estimation; identification; integration

摘要

Application of nonparametric and semiparametric regression techniques to high-dimensional time series data has been hampered due to the lack of effective tools to address the curse of dimensionality. Under rather weak conditions, we propose spline-backfitted kernel estimators of the component functions for the nonlinear additive time series data that are both computationally expedient so they are usable for analyzing very high-dimensional time series, and theoretically reliable so inference can be made on the component functions with confidence. Simulation experiments have provided strong evidence that corroborates the asymptotic theory.