A hybrid Newton-type method for censored survival data using double weights in linear models

DOI10.1007/s10985-006-9014-0zbMath1356.62193OpenAlexW2084408784WikidataQ31055190 ScholiaQ31055190MaRDI QIDQ5963031

Bin Nan, Menggang Yu

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/s10985-006-9014-0

zbMATH Keywords

linear programmingtwo-stage designcase-cohort designcensored linear regressiondouble weightsgeneralized Gehan-type weightshybrid Newton-type methodmonotone estimating function

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Applications of statistics to biology and medical sciences; meta analysis (62P10) Nonparametric estimation (62G05) Estimation in survival analysis and censored data (62N02)

Related Items (7)

Asymptotic theory for the semiparametric accelerated failure time model with missing data ⋮ Buckley-James Type Estimator for Censored Data with Covariates Missing by Design ⋮ Nonparametric quasi-likelihood for right censored data ⋮ Fast Censored Linear Regression ⋮ Regression Calibration in Semiparametric Accelerated Failure Time Models ⋮ Doubly Penalized Buckley–James Method for Survival Data with High‐Dimensional Covariates ⋮ Estimating Mean Survival Time: When is it Possible?

Cites Work

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• Accelerated failure time models for counting processes
• Rank-based inference for the accelerated failure time model
• Calibration Regression of Censored Lifetime Medical Cost
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