Reducing variability of crossvalidation for smoothing-parameter choice

DOI10.1093/biomet/asn068zbMath1163.62026OpenAlexW2168106919WikidataQ57060156 ScholiaQ57060156MaRDI QIDQ3613161

Andrew P. Robinson

Publication date: 11 March 2009

Published in: Biometrika (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1093/biomet/asn068

zbMATH Keywords

bandwidthkernel estimationnonparametric regressionsubsamplingbaggingnonparametric density estimationhalf-samplingstatistical smoothingbootstrap aggregation

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Density estimation (62G07) Nonparametric statistical resampling methods (62G09)

Related Items (12)

Performance of Robust GCV and Modified GCV for Spline Smoothing ⋮ On discrete Epanechnikov kernel functions ⋮ Improving cross-validated bandwidth selection using subsampling-extrapolation techniques ⋮ Extrapolation‐based Bandwidth Selectors: A Review and Comparative Study with Discussion on Bivariate Applications ⋮ Subsampling-extrapolation bandwidth selection in bivariate kernel density estimation ⋮ Unnamed Item ⋮ Analysis of multivariate longitudinal data using dynamic lasso-regularized copula models with application to large pediatric cardiovascular studies ⋮ A general class of linearly extrapolated variance estimators ⋮ A weighted least-squares cross-validation bandwidth selector for kernel density estimation ⋮ Optimality of training/test size and resampling effectiveness in cross-validation ⋮ Computational efficiency of bagging bootstrap bandwidth selection for density estimation with big data ⋮ Comments on ``Data science, big data and statistics

Uses Software

• R

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