Semiparametric random censorship models for survival data with long-term survivors

DOI10.1080/03610918.2018.1529239zbMath1489.62304OpenAlexW2906879579WikidataQ128688842 ScholiaQ128688842MaRDI QIDQ5083904

Xian Zhou, Yan Feng, Xiao Bing Zhao

Publication date: 21 June 2022

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2018.1529239

zbMATH Keywords

empirical processlong-term survivorlocal likelihood estimationsemiparametric random censorshipU-statistical processes

Mathematics Subject Classification ID

Density estimation (62G07) Asymptotic properties of nonparametric inference (62G20) Censored data models (62N01) Estimation in survival analysis and censored data (62N02)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• On semiparametric random censorship models
• The strong law under random censorship
• Local likelihood and local partial likelihood in hazard regression
• A semiparametric model for truncated and censored data
• Advances in survival analysis.
• \(U\)-statistic processes: A martingale approach
• Bootstrapping local polynomial estimators in likelihood-based models
• Local Likelihood Estimation
• Nonparametric Estimation from Incomplete Observations
• Asymptotics of kernel estimators based on local maximum likelihood
• Estimating the proportion of immunes in a censored sample
• Local Maximum Likelihood Estimation and Inference
• Local Polynomial Estimation in Multiparameter Likelihood Models
• Asymptotically efficient estimation of a survival function in the missing censoring indicator model
• Local Polynomial Kernel Regression for Generalized Linear Models and Quasi-Likelihood Functions

This page was built for publication: Semiparametric random censorship models for survival data with long-term survivors