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Inference on covariance operators via concentration inequalities: k-sample tests, classification, and clustering via Rademacher complexities

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 نشر من قبل Adam Kashlak
 تاريخ النشر 2016
  مجال البحث الاحصاء الرياضي
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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.



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