PROJECTION-PURSUIT BASED PRINCIPAL COMPONENT ANALYSIS:A LARGE SAMPLE THEORY

作     者：Jian ZHANG Institute of Mathematics,Statistics and Actuarial Science,University of Kent,Canterbury,Kent CT2 7NF,U.K. Institute of Systems Science,Academy of Mathematics and Systems Science,Chinese Academy of Sciences,Beijing 100080,China 

作者机构：Institute of Mathematics Statistics and Actuarial Science University of Kent Canterbury Kent U.K. Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China 

基　　金：The researcb was partially supported by the National Natural Science Foundation of China under Grant No.19631040 

出 版 物：《Journal of Systems Science & Complexity》 (系统科学与复杂性学报（英文版）)

年 卷 期：2006年第19卷第3期

页      码：365-385页

摘      要：The principal component analysis （PCA） is one of the most celebrated methods in analysing multivariate data. An effort of extending PCA is projection pursuit （PP）, a more general class of dimension-reduction techniques. However, the application of this extended procedure is often hampered by its complexity in computation and by lack of some appropriate theory. In this paper, by use of the empirical processes we established a large sample theory for the robust PP estimators of the principal components and dispersion matrix.

主 题 词：Dispersion matrices eigenvalues and eigenvectors empirical processes principal component analysis projection pursuit (PP). 

学科分类：02[经济学] 0202[经济学-财政学类] 020208[020208] 07[理学] 0714[0714] 070103[070103] 0701[理学-数学类] 

核心收录：

D　O　I：10.1007/s11424-006-0365-0

馆 藏 号：203249960...