Checking the linear transformation model for clustered failure time observations

DOI10.1007/S10985-008-9082-4zbMath1356.62200OpenAlexW2010904636WikidataQ80606863 ScholiaQ80606863MaRDI QIDQ5963034

Satoshi Hattori

Publication date: 25 February 2016

Published in: Lifetime Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10985-008-9082-4

zbMATH Keywords

Gaussian processproportional odds modelmartingale residuals

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Applications of statistics to biology and medical sciences; meta analysis (62P10) Inference from stochastic processes (62M99) Reliability and life testing (62N05)

Cites Work

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• The Kaplan-Meier estimate for dependent failure time observations
• Towards a general asymptotic theory for Cox model with staggered entry
• A large sample study of rank estimation for censored regression data
• Semiparametric regression analysis for clustered failure time data
• Martingale-based residuals for survival models
• Residuals for relative risk regression
• Analysis of transformation models with censored data
• Checking the marginal Cox model for correlated failure time data
• Checking the Cox model with cumulative sums of martingale-based residuals
• Semiparametric analysis of the additive risk model
• Predicting Survival Probabilities With Semiparametric Transformation Models
• Marginal Regression Models for Multivariate Failure Time Data
• Linear Regression Analysis for Highly Stratified Failure Time Data

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