Asymptotically efficient estimation of a survival function in the missing censoring indicator model

DOI10.1080/10485250410001681176zbMath1049.62111OpenAlexW2090710510MaRDI QIDQ4820848

Sundarraman Subramanian

Publication date: 1 October 2004

Published in: Journal of Nonparametric Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10485250410001681176

zbMATH Keywords

kernel density estimatormean integrated squared error\(U\)-statisticslimit theorybandwidth sequencereduced-data nonparametric maximum likelihood estimator

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)

Related Items (22)

Empirical likelihood and variable selection for partially linear single-index EV models with missing censoring indicators ⋮ The multiple imputations based Kaplan-Meier estimator ⋮ Quantile regression for partially linear varying-coefficient model with censoring indicators missing at random ⋮ Weighted composite quantile regression with censoring indicators missing at random ⋮ Semiparametric random censorship models for survival data with long-term survivors ⋮ Multiple imputations and the missing censoring indicator model ⋮ Nonparametric estimation for survival data with censoring indicators missing at random ⋮ Hazard function estimation with cause-of-death data missing at random ⋮ Semiparametric inference of competing risks data with additive hazards and missing cause of failure under MCAR or MAR assumptions ⋮ Presmoothed estimation of the density function with truncated and censored data ⋮ The missing censoring indicator model and the smoothed bootstrap ⋮ Doubly robust semiparametric estimation for the missing censoring indicator model ⋮ Inverse censoring weighted median regression ⋮ Additive hazards regression with censoring indicators missing at random ⋮ A kernel-assisted imputation estimating method for the additive hazards model with missing censoring indicator ⋮ Survival analysis for the missing censoring indicator model using kernel density estimation techniques ⋮ Hazard function estimation from homogeneous right censored data with missing censoring indicators ⋮ CLT for integrated square error of density estimators with censoring indicators missing at random ⋮ Probability density estimation for survival data with censoring indicators missing at random ⋮ Linear regression analysis of survival data with missing censoring indicators ⋮ Quantile regression and variable selection for partially linear single-index models with missing censoring indicators ⋮ Estimation and inferences for varying coefficient partially nonlinear quantile models with censoring indicators missing at random

Cites Work

• The estimation of the hazard function from randomly censored data by the kernel method
• Kernel density and hazard function estimation in the presence of censoring
• On Kullback-Leibler loss and density estimation
• A law of the logarithm for kernel density estimators
• Estimating a survival function with incomplete cause-of-death data
• On semiparametric random censorship models
• On the least squares cross-validation bandwidth in hazard rate estimation
• Efficient estimation from right-censored data when failure indicators are missing at random
• Unified methods for censored longitudinal data and causality
• Bandwidth choice for nonparametric hazard rate estimation
• A survey of product-integration with a view toward application in survival analysis
• A Brief Survey of Bandwidth Selection for Density Estimation
• Approximation Theorems of Mathematical Statistics
• Bootstrap choice of the smoothing parameter in kernel density estimation
• Bootstrap Selection of the Smoothing Parameter in Nonparametric Hazard Rate Estimation
• Multiple imputation methods for testing treatment differences in survival distributions with missing cause of failure
• Product‐limit Estimators and Cox Regression with Missing Censoring Information
• Median regression and the missing information principle
• Median regression using nonparametric kernel estimation

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