Kernel bandwidth selection for a first order nonparametric streamflow simulation model
type: Item
instance of: scholarly article
title: Kernel bandwidth selection for a first order nonparametric streamflow simulation model
zbMATH Open document ID: 0935.76072
author: K. Appert
published in: Stochastic Hydrology and Hydraulics
publication date: 2000-05-14T00:00:00Z
review text: This study documents some of the tests that were conduced to evaluate the performance of bandwidth estimation methods for kernel density estimation. We examine issues related to selection of optimal smoothing parameters for kernel density estimation with small samples (200 or fewer data points). Both reference to a Gaussian density and data based specifications are applied to estimate bandwidths for samples from bivariate normal mixture densities. The three data based methods studied are maximum likelihood cross-validation, least-square cross-validation, and biased cross-validation.
Mathematics Subject Classification ID: 76M35
Mathematics Subject Classification ID: 86A05
zbMATH DE Number: 1282008
zbMATH Keywords: bandwidth estimation methods
zbMATH Keywords: kernel density estimation
zbMATH Keywords: optimal smoothing parameters
zbMATH Keywords: Gaussian density
zbMATH Keywords: maximum likelihood cross-validation
zbMATH Keywords: least-square cross-validation
zbMATH Keywords: biased cross-validation
describes a project that uses: KernSmooth
MaRDI profile type: Publication
Wikidata QID: Q58092046
full work available at URL: s004770050008
OpenAlex ID: W2019825892
DOI: 10.1007/S004770050008
description: scientific article; zbMATH DE number 1282008