文章基本信息

标题：Inference on Covariance Operators via Concentration Inequalities: k-sample Tests, Classification, and Clustering via Rademacher Complexities
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作者：Adam B. Kashlak ; John A. D. Aston ; Richard Nickl 等
期刊名称：Sankhya. Series A, mathematical statistics and probability
印刷版ISSN：0976-836X
电子版ISSN：0976-8378
出版年度：2018
页码：1-30
DOI：10.1007/s13171-018-0143-9
语种：English
出版社：Indian Statistical Institute
摘要：We propose a novel approach to the analysis of covariance operators making use of concentration inequalities. First, non-asymptotic confidence sets are constructed for such operators. Then, subsequent applications including a k sample test for equality of covariance, a functional data classifier, and an expectation-maximization style clustering algorithm are derived and tested on both simulated and phoneme data.
关键词：Functional data analysis ; Manifold data ; Non-asymptotic confidence sets ; Concentration of measure.