CONFIDENCE BANDS IN NONPARAMETRIC REGRESSION

• 成果类型: Article
• 署名作者: EUBANK, RL; SPECKMAN, PL
• 署名单位: University of Missouri System; University of Missouri Columbia
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.2307/2291269
• 发表日期: 1993
• 页码: 1287-1301
• 关键词: smoothing spline; variance; deviations; intervals; average; error

摘要

New bias-corrected confidence bands are proposed for nonparametric kernal regression. These bands are constructed using only a kernel estimator of the regression curve and its data-selected bandwidth. They are shown to have asymptotically correct coverage properties and to behave well in a small-sample study. One consequence of the large-sample developments is that Bonferroni-type bands for the regression curve at the design points also have conservative asymptotic coverage behavior with no bias correction.