On the asymptotic normality of the kernel estimators of the density function and its derivatives under censoring

DOI10.1080/03610929808832263zbMath0924.62044OpenAlexW1976164437MaRDI QIDQ4246296

Djamal Louani

Publication date: 9 November 1999

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610929808832263

zbMATH Keywords

asymptotic normalitykernel estimatorsdensityderivativessimulationscensored datamodeKaplan Meier estimator

Mathematics Subject Classification ID

Density estimation (62G07) Asymptotic properties of nonparametric inference (62G20)

Related Items (12)

Local polynomial smoothing based on the Kaplan-Meier estimate ⋮ Asymptotic properties of the kernel mode estimator under twice censorship model ⋮ Strong Consistency Rate for the Kernel Mode Estimator Under Strong Mixing Hypothesis and Left Truncation ⋮ On the strong uniform consistency of the mode estimator for censored time series ⋮ ASYMPTOTIC BEHAVIOR OF A KERNEL CONDITIONAL MODE ESTIMATOR FOR LEFT TRUNCATED AND RIGHT CENSORED DATA ⋮ Strong uniform consistency of nonparametric estimation of the censored conditional mode function ⋮ Asymptotic normality for estimator of conditional mode under left-truncated and dependent observations ⋮ Uniform rate of strong consistency for a smooth kernel estimator of the conditional mode for censored time series ⋮ Some asymptotic properties for a smooth kernel estimator of the conditional mode under random censorship ⋮ A review of uncertainty quantification for density estimation ⋮ A new proof of strong consistency of kernel estimation of density function and mode under random censorship. ⋮ Kernel estimators of mode under \(\psi\)-weak dependence

Cites Work

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• Strong consistency properties of nonparametric estimators for randomly censored data. I: The product-limit estimator
• Optimum kernel estimators
• Strong consistency properties of nonparametric estimators for randomly censored data. II: Estimation of density and failure rate
• Bandwidth selection for kernel density estimation
• On the least squares cross-validation bandwidth in hazard rate estimation
• The central limit theorem under random censorship
• Nonparametric Estimation from Incomplete Observations
• Nonparametric density estimation from censored data
• Non-parametric estimation of the conditional mode
• Incorporating support constraints into nonparametric estimators of densities
• Biased and Unbiased Cross-Validation in Density Estimation
• The asymptotic distributions of kernel estimators of the mode
• Weak and strong uniform consistency rates of kernel density estimates for randomly censored data
• Mean squared errors of estimates of a density and its derivatives
• On Strong Consistency of Density Estimates
• On Estimation of a Probability Density Function and Mode

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