Weak representation of the cumulative hazard function under semiparametric random censorship models

DOI10.1080/02331880108802743zbMath1002.62076OpenAlexW1992345451MaRDI QIDQ4547511

Gerhard Dikta

Publication date: 13 January 2003

Published in: Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02331880108802743

zbMATH Keywords

maximum likelihood estimationKaplan-Meier estimatorrandom censorshipsemiparametric estimatorweak representation

Mathematics Subject Classification ID

Asymptotic properties of nonparametric inference (62G20) Nonparametric estimation (62G05) Estimation in survival analysis and censored data (62N02)

Related Items (5)

The central limit theorem under semiparametric random censorship models ⋮ Nonparametric estimation of the conditional distribution function in a semiparametric censorship model ⋮ 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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• Some properties of the Kaplan-Meier estimator for independent nonidentically distributed random variables
• On semiparametric random censorship models
• Strong and weak representations of cumulative hazard function and Kaplan- Meier estimators on increasing sets
• A large sample study of the life table and product limit estimates under random censorship
• The central limit theorem under random censorship
• The strong law under semiparametric random censorship models
• Nonparametric Estimation from Incomplete Observations

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