Kernel estimation of a smooth distribution function based on censored data
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Publication:910127
DOI10.1007/BF02613509zbMath0695.62105OpenAlexW1975529407MaRDI QIDQ910127
Publication date: 1990
Published in: Metrika (Search for Journal in Brave)
Full work available at URL: https://eudml.org/doc/176274
kernel estimatorsKaplan-Meier estimatorasymptotic performancesmoothness conditionssmooth distribution functionrandomly right censored datarelative deficiencyStrong uniform consistencyweak convergence of the normalized process
Related Items (7)
Some heuristics about bandwidth selection for the smooth Kaplan-Meier estimator ⋮ Bandwidth choice for the smooth Kaplan–Meier estimator when the censoring variable can be discontinuous ⋮ \(\mathcal L_1\)-deficiency of the sample quantile estimator with respect to a kernel quantile estimator ⋮ Efficient estimation of a distribution function based on censored data ⋮ An em algorithm for density estimation with randomly censored data ⋮ The strong uniform consistency of kernel estimator of a smooth distribution function in censored linear regression ⋮ Comparisons Between Local Linear Estimator and Kernel Smooth Estimator for a Smooth Distribution Based on MSE Under Right Censoring
Cites Work
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- Strong embedding of the estimator of the distribution function under random censorship
- Nonparametric Estimation from Incomplete Observations
- Relative efficiency and deficiency of kernel type estimators of smooth distribution functions
- Nonparametric estimates of distribution functions
- Sequential estimation of the mean survival time under random censorship
- The rate of strong uniform consistency for the product-limit estimator
- Strong uniform consistency of integrals of density estimators
- Convergence rate of perturbed empirical distribution functions
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