The benefit of data-based model complexity selection via prediction error curves in time-to-event data

DOI10.1007/S00180-011-0236-6zbMath1304.65061OpenAlexW2068241138MaRDI QIDQ63246

Harald Binder, Christine Porzelius, Martin Schumacher, Christine Porzelius, Harald Binder, Martin Schumacher

Publication date: 12 February 2011

Published in: Computational Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00180-011-0236-6

zbMATH Keywords

model selectionmodel complexitytime-to-event datarandom survival forestsprediction error curves

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08)

Related Items (1)

peperr

Uses Software

• Unnamed Item
• snowfall
• randomSurvivalForest
• peperr
• ElemStatLearn

Cites Work

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• Unnamed Item
• Random survival forests
• Adapting Prediction Error Estimates for Biased Complexity Selection in High-Dimensional Bootstrap Samples
• Improvements on Cross-Validation: The .632+ Bootstrap Method
• High-Dimensional Variable Selection for Survival Data
• Strictly Proper Scoring Rules, Prediction, and Estimation
• Efron‐Type Measures of Prediction Error for Survival Analysis
• Random forests

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