Validation of the alternating conditional estimation algorithm for estimation of flexible extensions of Cox's proportional hazards model with nonlinear constraints on the parameters

DOI10.1002/BIMJ.201500035zbMath1353.62131OpenAlexW2507198278WikidataQ39464581 ScholiaQ39464581MaRDI QIDQ2833481

Willy Wynant, Michał Abrahamowicz

Publication date: 18 November 2016

Published in: Biometrical Journal (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/bimj.201500035

zbMATH Keywords

constrained optimizationtime-varying effectsCox's proportional hazards modelnonlinear effectsnonidentifiability

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) 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)

Uses Software

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Cites Work

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• Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models
• Choosing initial values for the EM algorithm for finite mixtures
• Maximum likelihood estimation via the ECM algorithm: A general framework
• Splines in Statistics
• Estimating Optimal Transformations for Multiple Regression and Correlation
• Time-Dependent Hazard Ratio: Modeling and Hypothesis Testing With Application in Lupus Nephritis
• Maximization by Parts in Likelihood Inference

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