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arXiv · 1604.06310

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

Adam B. Kashlak·John A. D. Aston·Richard Nickl
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Abstract

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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Adam B. Kashlak, John A. D. Aston, Richard Nickl. 2016-04-21. Inference on covariance operators via concentration inequalities: k-sample tests, classification, and clustering via Rademacher complexities. https://doi.org/10.1007/s13171-018-0143-9

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