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  • 标题:Inference on Covariance Operators via Concentration Inequalities: k-sample Tests, Classification, and Clustering via Rademacher Complexities
  • 本地全文:下载
  • 作者:Adam B. Kashlak ; John A. D. Aston ; Richard Nickl
  • 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:2019
  • 卷号:81
  • 期号:1
  • 页码:214-243
  • DOI:10.1007/s13171-018-0143-9
  • 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.
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