The central limit theorem under semiparametric random censorship models

DOI10.1016/j.jspi.2003.09.013zbMath1056.60021OpenAlexW2050385517MaRDI QIDQ1888827

Christian Schmidt, J. K. Ghorai, Gerhard Dikta

Publication date: 29 November 2004

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.2003.09.013

zbMATH Keywords

Maximum likelihood estimationcentral limit theoremCensored dataProportional hazards modelKaplan-Meier estimator

Mathematics Subject Classification ID

Asymptotic distribution theory in statistics (62E20) Central limit and other weak theorems (60F05)

Related Items

Semi-parametric survival function estimators deduced from an identifying Volterra type integral equation ⋮ Asymptotically efficient estimation under semi-parametric random censorship models ⋮ Presmoothing the Aalen-Johansen estimator in the illness-death model ⋮ Semi-parametric Random Censorship Models ⋮ Two-sample location-scale estimation from semiparametric random censorship models ⋮ Nonparametric estimation of the conditional distribution function in a semiparametric censorship model ⋮ Semiparametric left truncation and right censorship models with missing censoring indicators ⋮ Presmoothing the transition probabilities in the illness-death model ⋮ A semiparametric estimator of the bivariate distribution function for censored gap times ⋮ Presmoothed Estimation with Left-Truncated and Right-Censored Data ⋮ Asymptotic representation of presmoothed Kaplan-Meier integrals with covariates in a semiparametric censorship model

Cites Work

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• Nonparametric Estimation from Incomplete Observations
• On the Strong Law of Large Numbers and Related Results for Quasi-Stationary Sequences
• Weak representation of the cumulative hazard function under semiparametric random censorship models
• Asymptotic normality under the koziol-green model
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