The strong law under semiparametric random censorship models

DOI10.1016/S0378-3758(99)00086-5zbMath0941.62055MaRDI QIDQ1969132

Gerhard Dikta

Publication date: 2 August 2000

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Mathematics Subject Classification ID

Asymptotic properties of nonparametric inference (62G20) Martingales with discrete parameter (60G42) Censored data models (62N01) Strong limit theorems (60F15)

Related Items (16)

Semiparametric censorship model with covariates ⋮ Semi-parametric survival function estimators deduced from an identifying Volterra type integral equation ⋮ The central limit theorem under semiparametric random censorship models ⋮ Weak representation of the cumulative hazard function under semiparametric random censorship models ⋮ Asymptotically efficient estimation under semi-parametric random censorship models ⋮ Presmoothing the Aalen-Johansen estimator in the illness-death model ⋮ Semi-parametric Random Censorship Models ⋮ Asymptotic-based bandwidth selection for the presmoothed density estimator with censored data ⋮ Nonparametric estimation of the conditional distribution function in a semiparametric censorship model ⋮ Presmoothed estimation of the density function with truncated and censored data ⋮ Strong consistency of presmoothed Kaplan–Meier integrals when covariables are present ⋮ Presmoothing the transition probabilities in the illness-death model ⋮ Resampling Methods for Testing a Semiparametric Random Censorship Model ⋮ Comparison of presmoothing methods in kernel density estimation under censoring ⋮ A semiparametric estimator of the bivariate distribution function for censored gap times ⋮ Chi-squared goodness-of-fit theory under proportional censorship

Cites Work

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• Maximum likelihood estimation of a survival function under the Koziol- Green proportional hazards model
• Strong consistency under the Koziol-Green model
• On semiparametric random censorship models
• The strong law under random censorship
• The central limit theorem under random censorship
• Nonparametric Estimation from Incomplete Observations

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