Estimation of mean residual life with censorded data under the proportional hazard model

DOI10.1080/03610928708829492zbMath0646.62029OpenAlexW1970695252MaRDI QIDQ3790467

J. K. Ghorai, Lídia Rejtő

Publication date: 1987

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610928708829492

zbMATH Keywords

truncationmean residual life functioncensored dataasymptotic confidence intervalmean square consistencyweak convergence to a Gaussian processproportional hazard rate modeluniform strong consistencyLarge sample propertiesmaximum likelihood estimator of the survival function

Mathematics Subject Classification ID

Asymptotic distribution theory in statistics (62E20) Applications of statistics to biology and medical sciences; meta analysis (62P10) Nonparametric estimation (62G05) Central limit and other weak theorems (60F05) Reliability and life testing (62N05)

Related Items (9)

Nonparametric bayesian estimation of a survival function under the proportional hazard model ⋮ Cramér-von mises statistic for testing goodness of fit under the proportional hazards model ⋮ Nonparametric restricted mean analysis across multiple follow-up intervals ⋮ Asymptotic normality under the koziol-green model ⋮ Nonparametric estimation of mean residual quantile function under right censoring ⋮ Estimation of a smooth quantile function under the proportional hazards model ⋮ On inference for mean residual life ⋮ Density estimation under the koziol‐green model of censoring by penalized likelihood methods ⋮ A central limit theorem for the weighted integrated squared error of the kernel type density estimator under the proportional hazard model

Cites Work

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• Estimation of a biometric function
• A large sample study of the life table and product limit estimates under random censorship
• The product-limit estimator and the bootstrap: Some asymptotic representations
• Nonparametric Estimation from Incomplete Observations
• Limit theorems for the ratio of the empirical distribution function to the true distribution function

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