Strong consistency of presmoothed Kaplan–Meier integrals when covariables are present

DOI10.1080/02331880412331316065zbMath1055.62052OpenAlexW1965386529MaRDI QIDQ4651104

Jacobo de Uña-Álvarez, M. Celia Rodríguez-Campos

Publication date: 21 February 2005

Published in: Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02331880412331316065

zbMATH Keywords

martingalescensoringcovariatespresmoothing

Mathematics Subject Classification ID

Asymptotic properties of nonparametric inference (62G20) Martingales with discrete parameter (60G42) Estimation in survival analysis and censored data (62N02)

Related Items (10)

A comparison of presmoothing methods in the estimation of transition probabilities ⋮ A presmooth estimator of unbiased distributions with length-biased data ⋮ Presmoothing the Aalen-Johansen estimator in the illness-death model ⋮ The Kaplan-Meier Integral in the Presence of Covariates: A Review ⋮ Nonparametric estimation of the conditional distribution function in a semiparametric censorship model ⋮ Nonparametric estimation of the survival function for ordered multivariate failure time data: A comparative study ⋮ Presmoothing the transition probabilities in the illness-death model ⋮ Comparison of presmoothing methods in kernel density estimation under censoring ⋮ 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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• Uniform consistency of a class of regression function estimators
• Strong consistency under the Koziol-Green model
• On semiparametric random censorship models
• The strong law under random censorship
• Strong consistency under proportional censorship when covariables are present
• The strong law under semiparametric random censorship models
• Consistent estimation under random censorship when covariables are present
• Nonparametric Estimation from Incomplete Observations
• Weak and strong uniform consistency of kernel regression estimates
• The uniform convergence of nearest neighbor regression function estimators and their application in optimization
• The uniform convergence of the nadaraya‐watson regression function estimate

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