Efficient and robust density estimation using Bernstein type polynomials
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Publication:2811278
DOI10.1080/10485252.2016.1163349zbMath1338.62084arXiv1404.7084OpenAlexW2312242748MaRDI QIDQ2811278
Publication date: 10 June 2016
Published in: Journal of Nonparametric Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1404.7084
robustnesssmoothingmodel selectionBernstein polynomialsefficiencymaximum likelihoodnonparametric modeldensity estimationchange-pointparametrisationbeta mixture
Density estimation (62G07) Asymptotic properties of nonparametric inference (62G20) Nonparametric estimation (62G05)
Related Items (13)
Wavelet change-point estimation for the density based on biased sample ⋮ An improved Hoeffding's inequality for sum of independent random variables ⋮ Maximum approximate Bernstein likelihood estimation in a two-sample semiparametric model ⋮ Choice of degree of Bernstein polynomial model ⋮ Maximum approximate likelihood estimation in accelerated failure time model for interval‐censored data ⋮ Unnamed Item ⋮ Pointwise wavelet estimation of density function with change-points based on NA and biased sample ⋮ Design‐based inference on Bernstein type estimators for continuous populations ⋮ Bernstein polynomial model for nonparametric multivariate density ⋮ Asymptotic properties of Bernstein estimators on the simplex ⋮ A review of uncertainty quantification for density estimation ⋮ Moderate deviation principles for nonparametric recursive distribution estimators using Bernstein polynomials ⋮ Regression-type analysis for multivariate extreme values
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