Rank-based inference for the accelerated failure time model in the presence of interval censored data

DOI10.3934/naco.2017007zbMath1357.62280OpenAlexW2589297101MaRDI QIDQ515173

Jayanthi Arasan, Mostafa Karimi, Noor Akma Ibrahim, Mohd Rizam Abu Bakar

Publication date: 13 March 2017

Published in: Numerical Algebra, Control and Optimization (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.3934/naco.2017007

zbMATH Keywords

survival analysissemiparametric analysisinterval censored dataaccelerated failure time modelrank-based inference

Mathematics Subject Classification ID

Censored data models (62N01) Nonparametric statistical resampling methods (62G09) Estimation in survival analysis and censored data (62N02) Computational methods for problems pertaining to operations research and mathematical programming (90-08)

Cites Work

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• Semiparametric estimation methods for the accelerated failure time mixture cure model
• A tutorial on rank-based coefficient estimation for censored data in small- and large-scale problems
• Estimation in a linear regression model with censored data
• Rank regression for accelerated failure time model with clustered and censored data
• Estimating regression parameters using linear rank tests for censored data
• Rank regression methods for left-truncated and right-censored data
• Large sample theory of a modified Buckley-James estimator for regression analysis with censored data
• Monotone estimating equations for censored data
• A large sample study of rank estimation for censored regression data
• Jackknife Empirical Likelihood for the Accelerated Failure Time Model with Censored Data
• A Semiparametric Model for Regression Analysis of Interval-Censored Failure Time Data
• Linear rank tests with right censored data
• Accelerated failure time models for counting processes
• Rank-based inference for the accelerated failure time model
• A generalized Wilcoxon test for comparing arbitrarily singly-censored samples
• Regression Modeling of Semicompeting Risks Data