A review of uncertainty quantification for density estimation
type: Item
instance of: scholarly article
title: A review of uncertainty quantification for density estimation
zbMATH Open document ID: 1497.62086
DOI: 10.1214/21-SS130
author: Yanyan Li
published in: Statistics Surveys
publication date: 2021-08-06T00:00:00Z
review text: It is important to conduct inference for an unknown probability density function by construction plausible sets in which the unknown density function is likely to be contained. Examples of such sets include pointwise intervals, simultaneous bands or balls in a function space depending on whether the density estimators are constructed via the frequentist or the Bayesian interpretation. The authors give a review of several methods and study the corresponding uncertainty quantification methods.
Mathematics Subject Classification ID: 62G07
Mathematics Subject Classification ID: 62-02
zbMATH DE Number: 7379433
zbMATH Keywords: nonparametric inference
zbMATH Keywords: density estimation
zbMATH Keywords: uncertainty quantification
reviewed by: B. L. S. Prakasa Rao
describes a project that uses: msBP
describes a project that uses: BayesDA
describes a project that uses: BNPdensity
describes a project that uses: R
describes a project that uses: DPWeibull
describes a project that uses: asciker
describes a project that uses: akdensity
MaRDI profile type: Publication
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A review of uncertainty quantification for density estimation (full work available at URL)
full work available at URL: 21-ss130

A review of uncertainty quantification for density estimation (OpenAlex ID)
OpenAlex ID: W3154675872

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