Nonparametric estimation of the conditional distribution function in a semiparametric censorship model

DOI10.1016/j.jspi.2007.11.011zbMath1140.62026OpenAlexW2014425305MaRDI QIDQ935436

Maria Carmen Iglesias-Pérez, Jacobo de Uña-Álvarez

Publication date: 6 August 2008

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

Full work available at URL: https://doi.org/10.1016/j.jspi.2007.11.011

zbMATH Keywords

kernel smoothinginformative censoringconditional Kaplan-Meier

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Asymptotic properties of nonparametric inference (62G20) Nonparametric estimation (62G05) Censored data models (62N01) Estimation in survival analysis and censored data (62N02)

Related Items (3)

Presmoothing the transition probabilities in the illness-death model ⋮ A semiparametric estimator of the bivariate distribution function for censored gap times ⋮ Asymptotic representation of presmoothed Kaplan-Meier integrals with covariates in a semiparametric censorship model

Cites Work

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• Chi-squared goodness-of-fit theory under proportional censorship
• The central limit theorem under semiparametric random censorship models
• The strong law under semiparametric random censorship models
• Nonparametric Estimation from Incomplete Observations
• Strong representation of a generalized product-limit estimator for truncated and censored data with some applications
• Weak representation of the cumulative hazard function under semiparametric random censorship models
• Presmoothed Kaplan–Meier and Nelson–Aalen estimators
• Strong consistency of presmoothed Kaplan–Meier integrals when covariables are present
• Presmoothed kernel density estimator for censored data

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