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ALMOST SURE CLASSIFICATION OF DENSITIES 

Authors: Luc Devroye a; Gaacutebor Lugosi b
Affiliations:   a School of Computer Science, McGill University, Montreal, Canada H3A 2A7.
b Department of Economics, Pompeu Fabra University, Ramon Trias Fargas, 25-27 08005 Barcelona, Spain.
DOI: 10.1080/10485250215323
Publication Frequency: 8 issues per year
Published in: journal Journal of Nonparametric Statistics, Volume 14, Issue 6 2002 , pages 675 - 698
Number of References: 28
Formats available: PDF (English)
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Abstract

Let a class lcub\cal Frcub of densities be given. We draw an i.i.d. sample from a density f which may or may not be in lcub\cal Frcub . After every n , one must make a guess whether f \in lcub\cal Frcub or not. A class is almost surely discernible if there exists such a sequence of classification rules such that for any f , we make finitely many errors almost surely. In this paper several results are given that allow one to decide whether a class is almost surely discernible. For example, continuity and square integrability are not discernible, but unimodality, log-concavity, and boundedness by a given constant are.
Keywords: Density Estimation; Kernel Estimate; Convergence; Discernibility; Hypothesis Testing; Asymptotic Optimality; Minimax Rate; Minimum Distance Estimation; Total Boundedness
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