Estimation of the Asymptotic Variance of Semiparametric Maximum Likelihood Estimators in the Cox Model with a Missing Time-Dependent Covariate

DOI10.1081/STA-120030156zbMath1114.62325OpenAlexW2092768630MaRDI QIDQ3155332

Jean-François Dupuy, Mounir Mesbah

Publication date: 14 January 2005

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

Full work available at URL: https://doi.org/10.1081/sta-120030156

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic properties of nonparametric inference (62G20) Nonparametric estimation (62G05)

Related Items (4)

Analysis of a semiparametric mixture model for competing risks ⋮ A note on variance estimation in the Cox proportional hazards model ⋮ Asymptotic theory for the Cox model with missing time-dependent covariate ⋮ The proportional hazards model with covariate measurement error

Cites Work

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• Asymptotic theory for the correlated gamma-frailty model
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• An Extension of Cox's Regression Model
• A Semiparametric Likelihood Approach to Joint Modeling of Longitudinal and Time-to-Event Data
• Cox Regression with Incomplete Covariate Measurements
• Regression Calibration in Failure Time Regression
• A Joint Model for Survival and Longitudinal Data Measured with Error
• On using the Cox proportional hazards model with missing covariates
• Proportional Hazards Regression with Missing Covariates
• Evaluating Surrogate Markers of Clinical Outcome When Measured with Error
• Auxiliary covariate data in failure time regression
• Joint modelling of longitudinal measurements and event time data
• An estimator for the proportional hazards model with multiple longitudinal covariates measured with error

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