Advanced distribution theory for SiZer

• 成果类型: Article
• 署名作者: Hannig, J.; Marron, J. S.
• 署名单位: Colorado State University System; Colorado State University Fort Collins; University of North Carolina; University of North Carolina Chapel Hill
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214505000001294
• 发表日期: 2006
• 页码: 484-499
• 关键词: asymptotic equivalence; Nonparametric Regression; bandwidth selection; extremes

摘要

SiZer is a powerful method for exploratory data analysis. In this article approximations to the distributions underlying the simultaneous statistical inference are investigated, and large improvements are made in the approximation using extreme value theory. This results in improved size, and also in an improved global inference version of SiZer. The main points are illustrated with real data and simulated examples.