ROBUST FUNCTIONAL PRINCIPAL COMPONENTS: A PROJECTION-PURSUIT APPROACH

• 成果类型: Article
• 署名作者: Lucas Bali, Juan; Boente, Graciela; Tyler, David E.; Wang, Jane-Ling
• 署名单位: University of Buenos Aires; University of California System; University of California Davis; Rutgers University System; Rutgers University New Brunswick
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/11-AOS923
• 发表日期: 2011
• 页码: 2852-2882
• 关键词: DISTRIBUTIONS; estimators; matrices; scale

摘要

In many situations, data are recorded over a period of time and may be regarded as realizations of a stochastic process. In this paper, robust estimators for the principal components are considered by adapting the projection pursuit approach to the functional data setting. Our approach combines robust projection-pursuit with different smoothing methods. Consistency of the estimators are shown under mild assumptions. The performance of the classical and robust procedures are compared in a simulation study under different contamination schemes.