ROBUST FUNCTIONAL PRINCIPAL COMPONENTS: A PROJECTION-PURSUIT APPROACH

被引：64

作者：

Lucas Bali, Juan ^{[1
]}

Boente, Graciela ^{[1
]}

Tyler, David E. ^{[3
]}

Wang, Jane-Ling ^{[2
]}

机构：

[1] Univ Buenos Aires, Fac Ciencias Exactas & Nat, Inst Calculo, RA-1428 Buenos Aires, DF, Argentina

[2] Univ Calif Davis, Dept Stat, Davis, CA 95616 USA

[3] Rutgers State Univ, Dept Stat, Hill Ctr, Piscataway, NJ 08854 USA

来源：

ANNALS OF STATISTICS | 2011年 / 39卷 / 06期

基金：

美国国家科学基金会;

关键词：

Fisher-consistency; functional data; method of sieves; penalization; principal component analysis; outliers; robust estimation; DISTRIBUTIONS; ESTIMATORS; MATRICES; SCALE;

D O I：

10.1214/11-AOS923

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

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.

引用

页码：2852 / 2882

页数：31

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