Inference on Covariance Operators via Concentration Inequalities: k-sample Tests, Classification, and Clustering via Rademacher Complexities
| Autor: | Richard Nickl, John A. D. Aston, Adam B. Kashlak |
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| Přispěvatelé: | Apollo - University of Cambridge Repository |
| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Statistics and Probability Manifold data Inference Mathematics - Statistics Theory Statistics Theory (math.ST) 01 natural sciences Methodology (stat.ME) 010104 statistics & probability FOS: Mathematics 62G05 0101 mathematics Cluster analysis Statistics - Methodology Mathematics Analysis of covariance Concentration of measure 010102 general mathematics Functional data analysis Covariance ComputingMethodologies_PATTERNRECOGNITION Non-asymptotic confidence sets Statistics Probability and Uncertainty Classifier (UML) Algorithm |
| Zdroj: | Sankhya A. 81:214-243 |
| ISSN: | 0976-8378 0976-836X |
| Popis: | 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. 15 pages, 2 figures, 6 tables |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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