An exact bootstrap approach towards modification of the Harrell–Davis quantile function estimator for censored data

DOI10.1080/10485250903524662zbMath1203.62048OpenAlexW2092426980MaRDI QIDQ3068119

Dongliang Wang, Daniel P. Gaile, Alan D. Hutson

Publication date: 13 January 2011

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

Full work available at URL: https://doi.org/10.1080/10485250903524662

zbMATH Keywords

bootstraporder statisticskernel quantile smoothing

Mathematics Subject Classification ID

Nonparametric estimation (62G05) Censored data models (62N01) Nonparametric statistical resampling methods (62G09) Monte Carlo methods (65C05) Algorithms with automatic result verification (65G20)

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Cites Work

• Unnamed Item
• On the asymptotic properties of a kernel type quantile estimator from censored samples
• Unified estimators of smooth quantile and quantile density functions
• Deficiency of the sample quantile estimator with respect to kernel quantile estimators for censored data
• Nonparametric Estimation from Incomplete Observations
• Kernel Quantile Estimators
• A Kernel-Type Estimator of a Quantile Function From Right-Censored Data
• Smooth nonparametric quantile estimation under censoring: simulations and bootstrap methods
• A new distribution-free quantile estimator
• Fractional Order Statistics, with Applications
• The Exact Bootstrap Mean and Variance of an L-estimator

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