Data‐driven choice of the smoothing parametrization for kernel density estimators

DOI10.1002/cjs.10016zbMath1176.62028OpenAlexW2040149518MaRDI QIDQ5192950

José E. Chacón

Publication date: 10 August 2009

Published in: Canadian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/cjs.10016

zbMATH Keywords

relative efficiencysmoothing parametrization

Mathematics Subject Classification ID

Related Items (14)

Non-parametric smoothed estimation of multivariate cumulative distribution and survival functions, and receiver operating characteristic curves ⋮ FFT-based fast bandwidth selector for multivariate kernel density estimation ⋮ Multivariate plug-in bandwidth selection with unconstrained pilot bandwidth matrices ⋮ Multivariate locally adaptive kernel density estimation ⋮ Subsampling-extrapolation bandwidth selection in bivariate kernel density estimation ⋮ Data-driven density derivative estimation, with applications to nonparametric clustering and bump hunting ⋮ Fast multi-feature image segmentation ⋮ A binomial model for the kernel density estimator and related inference ⋮ A population background for nonparametric density-based clustering ⋮ UNCONSTRAINED PILOT SELECTORS FOR SMOOTHED CROSS-VALIDATION ⋮ Root \(n\) estimates of vectors of integrated density partial derivative functionals ⋮ A hybrid bandwidth selection methodology for kernel density estimation ⋮ Maximum Likelihood Estimation of a Multi-Dimensional Log-Concave Density ⋮ Nonparametrically consistent depth-based classifiers

Uses Software

• KernSmooth

Cites Work

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• Extent to which least-squares cross-validation minimises integrated square error in nonparametric density estimation
• An asymptotically optimal window selection rule for kernel density estimates
• Exact mean integrated squared error
• Convergence rates for unconstrained bandwidth matrix selectors in multivariate kernel density estimation
• Multivariate plug-in bandwidth selection
• Bootstrapping the mean integrated squared error
• Estimation of a multivariate density
• Relative efficiency of local bandwidths in kernel density estimation^∗
• Error analysis for general multtvariate kernel estimators
• Bootstrap Bandwidth Selection Using an h‐Dependent Pilot Bandwidth
• Cross-Validation of Multivariate Densities
• Plug-in bandwidth matrices for bivariate kernel density estimation
• Comparison of Smoothing Parameterizations in Bivariate Kernel Density Estimation
• Cross-validation Bandwidth Matrices for Multivariate Kernel Density Estimation

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