Marshall’s lemma for convex density estimation

DOI10.1214/074921707000000292zbMath1176.62029arXivmath/0609277OpenAlexW1565244706MaRDI QIDQ5324533

Kaspar Rufibach, Jon A. Wellner, Lutz Dümbgen

Publication date: 3 August 2009

Published in: Institute of Mathematical Statistics Lecture Notes - Monograph Series (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/math/0609277

zbMATH Keywords

inequalitymaximum likelihoodleast squaresdistribution functionshape constraintsupremum norm

Mathematics Subject Classification ID

Density estimation (62G07) Asymptotic properties of nonparametric inference (62G20) Order statistics; empirical distribution functions (62G30)

Related Items (10)

Maximum likelihood estimation of a log-concave density and its distribution function: basic properties and uniform consistency ⋮ On univariate convex regression ⋮ Theoretical properties of the log-concave maximum likelihood estimator of a multidimensional density ⋮ Some developments in the theory of shape constrained inference ⋮ A conversation with Jon Wellner ⋮ A second Marshall inequality in convex estimation ⋮ On convex least squares estimation when the truth is linear ⋮ Adaptation in log-concave density estimation ⋮ Shape constrained kernel density estimation ⋮ Marshall lemma in discrete convex estimation

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