Journal of Statistical Computation and Simulation · 2011 · 25 citations · 15 references
Public HealthBiostatisticsEpidemiologic MethodsStatistical LearningSurvival AnalysisEnsemble LearningEnsemble MethodsEnsemble ModelingModel SelectionSurvival PredictionVariable SelectionAccuracyEnsemble RegressionEnsemble SelectionEnsemble-based PredictionExplanatory Variable SelectionMulti-model EnsemblesSelection Decision AccuracySurvival Model Selection
Abstract Using Cox regression as the main platform, we study the ensemble approach for variable selection. We use a popular real-data example as well as simulated data with various censoring levels to illustrate the usefulness of the ensemble approach, and study the nature of these ensembles in terms of their strength and diversity. By relating these characteristics to the ensemble's selection accuracy, we provide useful insights for how to choose among different ensemble strategies, as well as guidelines for thinking about how to design more effective ensembles. Keywords: bootstrapCox regressionensemble learningsurvival analysisstrength-diversity trade-offvariable selection Acknowledgements This work is partially supported by the Natural Sciences and Engineering Research Council (NSERC) of Canada.
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