Variable selection by ensembles for the Cox model

DOI10.1080/00949655.2010.511622zbMath1431.62179OpenAlexW2010741163MaRDI QIDQ5300728

Mu Zhu, Guangzhe Fan

Publication date: 28 June 2013

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2010.511622

zbMATH Keywords

bootstrapsurvival analysisCox regressionvariable selectionensemble learningstrength-diversity trade-off

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Censored data models (62N01) Bayesian inference (62F15) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (5)

A novel bagging approach for variable ranking and selection via a mixed importance measure ⋮ PBoostGA: pseudo-boosting genetic algorithm for variable ranking and selection ⋮ RandGA: injecting randomness into parallel genetic algorithm for variable selection ⋮ Pruning variable selection ensembles ⋮ A fast adaptive Lasso for the cox regression via safe screening rules

Uses Software

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Cites Work

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• Greedy function approximation: A gradient boosting machine.
• Optimization by Simulated Annealing
• Estimating the dimension of a model
• Bayesian model averaging: A tutorial. (with comments and a rejoinder).
• Additive logistic regression: a statistical view of boosting. (With discussion and a rejoinder by the authors)
• Variable selection for Cox's proportional hazards model and frailty model
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Bayesian Variable Selection Method for Censored Survival Data
• Random forests

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