A hybrid bandwidth selection methodology for kernel density estimation

DOI10.1080/00949655.2012.721366zbMath1453.62444OpenAlexW2035953679MaRDI QIDQ5219286

Serge B. Provost, Min Jiang

Publication date: 9 March 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2012.721366

zbMATH Keywords

kernelsbandwidth selectiondensity estimationleast-squares cross-validationplug-in estimates

Mathematics Subject Classification ID

Density estimation (62G07) Approximations to statistical distributions (nonasymptotic) (62E17)

Related Items (2)

A data-driven kernel estimator of the density function ⋮ The generalized Pearson family of distributions and explicit representation of the associated density functions

Uses Software

• alr3
• KernSmooth

Cites Work

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• Feature significance for multivariate kernel density estimation
• An asymptotically optimal window selection rule for kernel density estimates
• Estimation of integrated squared density derivatives
• Exact mean integrated squared error
• On bandwidth variation in kernel estimates. A square root law
• Smoothing methods in statistics
• Bandwidth selection: Classical or plug-in?
• Variable Kernel Estimates of Multivariate Densities
• KERNEL DENSITY ESTIMATION WHEN THE BANDWIDTH IS LARGE
• Data‐driven choice of the smoothing parametrization for kernel density estimators

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