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No empirical probability measure can converge in the total variation sense for all distributions

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Publication:919702
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DOI10.1214/aos/1176347765zbMath0707.60026OpenAlexW2055288598WikidataQ101208116 ScholiaQ101208116MaRDI QIDQ919702

László Györfi, Luc P. Devroye

Publication date: 1990

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1214/aos/1176347765


zbMATH Keywords

empirical probability measuressingular continuous probability measure


Mathematics Subject Classification ID

Nonparametric estimation (62G05) Distribution theory (60E99)


Related Items (10)

A new class of metric divergences on probability spaces and its applicability in statistics ⋮ Estimation of a time-dependent density ⋮ Estimation of a distribution from data with small measurement errors ⋮ Adaptive Estimation of a Conditional Density ⋮ Using atomic bounds to get sub-modular approximations ⋮ Unnamed Item ⋮ On estimation of generalized densities ⋮ A semi-Bayesian method for nonparametric density estimation. ⋮ Distribution Estimates Consistent in χ2-Divergence ⋮ On density estimation from ergodic processes




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