LOCAL LINEAR-REGRESSION SMOOTHERS AND THEIR MINIMAX EFFICIENCIES

• 成果类型: Article
• 署名作者: FAN, JQ
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/aos/1176349022
• 发表日期: 1993
• 页码: 196-216
• 关键词: models; RISK

摘要

In this paper we introduce a smooth version of local linear regression estimators and address their advantages. The MSE and MISE of the estimators are computed explicitly. It turns out that the local linear regression smoothers have nice sampling properties and high minimax efficiency-they are not only efficient in rates but also nearly efficient in constant factors. In the nonparametric regression context, the asymptotic minimax lower bound is developed via the heuristic of the ''hardest one-dimensional subproblem'' of Donoho and Liu. Connections of the minimax risk with the modulus of continuity are made. The lower bound is also applicable for estimating conditional mean (regression) and conditional quantiles for both fixed and random design regression problems.