Information bounds for Cox regression models with missing data.

DOI10.1214/009053604000000157zbMath1097.62097arXivmath/0406452OpenAlexW3103403033MaRDI QIDQ1879951

Bin Nan, Jon A. Wellner, Mary J. Emond

Publication date: 15 September 2004

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/math/0406452

zbMATH Keywords

stratificationintegral equationsurvival analysisinformation boundCox modelregression modelsmissing at randomcase-cohort designefficient scorescorestangent spacetangent setefficient influence functionleast favorable directionmartingale operatorsmean residual life operators

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Approximations to statistical distributions (nonasymptotic) (62E17) Applications of functional analysis in probability theory and statistics (46N30) Estimation in survival analysis and censored data (62N02)

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Z-estimation and stratified samples: application to survival models, Information bounds for Cox regression models with missing data., Buckley-James Type Estimator for Censored Data with Covariates Missing by Design, Alternative expectation approaches for expectation-maximization missing data imputations in cox regression, Large sample theory for semiparametric regression models with two-phase, outcome dependent sampling., Weighted Likelihood for Semiparametric Models and Two‐phase Stratified Samples, with Application to Cox Regression, Information bounds and efficient estimation in a class of censored transformation models, Estimation and Inference Based on Neumann Series Approximation to Locally Efficient Score in Missing Data Problems, Retrospective likelihood-based methods for analyzing case-cohort genetic association studies, Semiparametric likelihood estimation in survival models with informative censoring, Relative risk regression for current status data in case-cohort studies, Representations of efficient score for coarse data problems based on Neumann series expansion

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