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 Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences 

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      面：365-385页

核心收录：

学科分类：02[经济学] 0202[经济学-应用经济学] 020208[经济学-统计学] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 070103[理学-概率论与数理统计] 0701[理学-数学] 

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

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

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.