Data-driven kernel representations for sampling with an unknown block dependence structure under correlation constraints
type: Item
instance of: scholarly article
title: Data-driven kernel representations for sampling with an unknown block dependence structure under correlation constraints
zbMATH Open document ID: 1469.62129
author: C. Soize
author: N. Ouhbi
author: G. Perrin
published in: Computational Statistics and Data Analysis
publication date: 2018-08-17T00:00:00Z
full work available at URL: publi-2017-JCSDA-perrin-soize-ouhbi-preprint.pdf
Mathematics Subject Classification ID: 62-08
Mathematics Subject Classification ID: 62G07
Mathematics Subject Classification ID: 62H12
zbMATH DE Number: 6920188
zbMATH Keywords: kernel density estimation
zbMATH Keywords: optimal bandwidth
zbMATH Keywords: nonparametric representation
zbMATH Keywords: data-driven sampling
describes a project that uses: BRENT
describes a project that uses: pyuvdata
describes a project that uses: KernSmooth
MaRDI profile type: Publication
OpenAlex ID: W2767356114
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DOI: 10.1016/J.CSDA.2017.10.005
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description: scientific article; zbMATH DE number 6920188