Local Smoothing Using the Bootstrap

DOI10.1080/03610918.2012.703360zbMath1333.62111OpenAlexW2076945467MaRDI QIDQ5417930

Santanu Dutta

Publication date: 23 May 2014

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2012.703360

zbMATH Keywords

scale invariancebandwidth selectionkernel density estimationMSEsmooth bootstrap

Mathematics Subject Classification ID

Density estimation (62G07) Asymptotic properties of nonparametric inference (62G20) Nonparametric statistical resampling methods (62G09)

Related Items (5)

Distribution function estimation via Bernstein polynomial of random degree ⋮ Pointwise and uniform convergence of kernel density estimators using random bandwidths ⋮ Density estimation via the random forest method ⋮ Cross-validation Revisited ⋮ Local Smoothing for Kernel Distribution Function Estimation

Uses Software

• pyuvdata

Cites Work

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• On nonparametric local inference for density estimation
• Bootstrap optimal bandwidth selection for kernel density estimates
• An optimal local bandwidth selector for kernel density estimation
• Bandwidth selection: Classical or plug-in?
• A data-based algorithm for choosing the window width when estimating the density at a point

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