The generalized moment estimation of the additive-multiplicative hazard model with auxiliary survival information

DOI10.1016/j.csda.2017.03.013zbMath1464.62158OpenAlexW2596779285MaRDI QIDQ1654269

Wenpeng Shang, Xiao Wang

Publication date: 7 August 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2017.03.013

zbMATH Keywords

survival analysisloss functionparametric estimationadditive-multiplicative hazardgeneralized momentsubgroup information

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Estimation in survival analysis and censored data (62N02)

Related Items (9)

Generalized method of moment for case-cohort under additive hazards model ⋮ Robust estimation for generalized case–cohort design under the proportional hazards model ⋮ Efficient estimation of a Cox model when integrating the subgroup incidence rate information ⋮ Case-cohort and inference for the proportional hazards model with covariate adjustment ⋮ Synthesizing secondary data into survival analysis to improve estimation efficiency ⋮ More effective estimation for additive hazards model in generalized case-cohort study ⋮ Unnamed Item ⋮ Additive hazards model with auxiliary subgroup survival information ⋮ Statistical inference for Cox model under case-cohort design with subgroup survival information

Cites Work

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• Large Sample Properties of Generalized Method of Moments Estimators
• Generalized method of moments estimation for cointegrated vector autoregressive models
• Improved GMM estimation of panel VAR models
• Semiparametric analysis of general additive-multiplicative hazard models for counting processes
• Generalized method of moments estimation for linear regression with clustered failure time data
• Semiparametric analysis of the additive risk model
• A partly parametric additive risk model
• A Model-Calibration Approach to Using Complete Auxiliary Information From Survey Data
• Statistical models based on counting processes

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