ALMOST SURE CLASSIFICATION OF DENSITIES
Authors:
Luc Devroye a;
G
bor Lugosi b
bor 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
Subjects:
Mathematical Economics;
Mathematical Finance;
Medical Statistics;
Statistical Theory & Methods;
Statistics;
Statistics for the Biological Sciences;
Stochastic Models & Processes;
Number of References: 28
Formats available:
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Abstract
Let a class
\cal F of densities be given. We draw an i.i.d. sample from a density f which may or may not be in \cal F . After every n , one must make a guess whether f \in \cal F 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.
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| Keywords: Density Estimation; Kernel Estimate; Convergence; Discernibility; Hypothesis Testing; Asymptotic Optimality; Minimax Rate; Minimum Distance Estimation; Total Boundedness |
| view references (28) |

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\cal F
of densities be given. We draw an i.i.d. sample from a density f which may or may not be in
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