Optimal Kernel Selection for Density Estimation

DOI10.1007/978-3-319-40519-3_19zbMath1356.62051arXiv1511.02112OpenAlexW2258773688MaRDI QIDQ2954056

Patricia Reynaud-Bouret, Nelo Molter Magalhães, Matthieu Lerasle

Publication date: 11 January 2017

Published in: High Dimensional Probability VII (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1511.02112

zbMATH Keywords

kernel estimatorsdensity estimationoracle inequalitiesminimal penaltyoptimal penalty

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Density estimation (62G07) Linear regression; mixed models (62J05) Nonparametric estimation (62G05) Non-Markovian processes: estimation (62M09) Applications of functional analysis in probability theory and statistics (46N30)

Related Items (5)

Should we estimate a product of density functions by a product of estimators? ⋮ Concentration inequality for U-statistics of order two for uniformly ergodic Markov chains ⋮ Unnamed Item ⋮ Kernel selection in nonparametric regression ⋮ Adaptive optimal kernel density estimation for directional data

Cites Work

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• A general result on the uniform in bandwidth consistency of kernel-type function estimators
• Uniform-in-bandwidth functional limit laws
• Optimal model selection in density estimation
• Bandwidth selection in kernel density estimation: oracle inequalities and adaptive minimax optimality
• Linear and convex aggregation of density estimators
• Uniform limit theorems for wavelet density estimators
• Adaptive density estimation using the blockwise Stein method
• Moment inequalities for U-statistics
• Concentration inequalities and model selection. Ecole d'Eté de Probabilités de Saint-Flour XXXIII -- 2003.
• Optimal convergence rates for Good's nonparametric maximum likelihood density estimator
• Density estimation by wavelet thresholding
• Erratum to: ``A general result on the uniform in bandwidth consistency of kernel-type function estimators
• Minimal penalties for Gaussian model selection
• Statistical estimation with model selection
• Concentration Inequalities
• Mathematical Foundations of Infinite-Dimensional Statistical Models
• Best asymptotic normality of the kernel density entropy estimator for smooth densities
• Functional Classification with Margin Conditions
• Introduction to nonparametric estimation
• Combinatorial methods in density estimation
• Maximum penalized likelihood estimation. Vol. 1: Density estimation

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