On nonparametric local inference for density estimation

DOI10.1016/j.csda.2009.09.021zbMath1464.62040OpenAlexW1999807858MaRDI QIDQ962279

Ngai Hang Chan, Liang Peng, Thomas C. M. Lee

Publication date: 6 April 2010

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2009.09.021

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Density estimation (62G07)

Related Items

Density estimation via the random forest method ⋮ New approaches to nonparametric density estimation and selection of smoothing parameters ⋮ Local Smoothing Using the Bootstrap

Uses Software

• KernSmooth
• pyuvdata

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Local data-driven bandwidth choice for density estimation
• Using the bootstrap to estimate mean squared error and select smoothing parameter in nonparametric problems
• Bias annihilating bandwidths for kernel density estimation at a point
• Poisson and Gaussian approximation of weighted local empirical processes
• An optimal local bandwidth selector for kernel density estimation
• Data sharpening methods for bias reduction in nonparametric regression.
• Skewing methods for two-parameter locally parametric density estimation
• A data-based algorithm for choosing the window width when estimating the density at a point
• A Diagnostic for Selecting the Threshold in Extreme Value Analysis
• A Brief Survey of Bandwidth Selection for Density Estimation
• Empirical likelihood confidence intervals for nonparametric density estimation
• High Order Data Sharpening for Density Estimation
• A New Characterization and Estimation of the Zero-bias Bandwidth
• A simple bias reduction method for density estimation
• Miscellanea. Data sharpening as a prelude to density estimation
• On local bootstrap bandwidth choice in kernel density estimation
• The bootstrap and Edgeworth expansion