The benefit of data-based model complexity selection via prediction error curves in time-to-event data
type: Item
instance of: scholarly article
published in: Computational Statistics
volume: 26
issue: 2
page(s): 293-302
publication date: 2011-02-12T00:00:00Z
publication date: 2015-01-28T00:00:00Z
author: Christine Porzelius
author: Martin Schumacher
author: Harald Binder
title: The benefit of data-based model complexity selection via prediction error curves in time-to-event data
zbMATH Open document ID: 1304.65061
Mathematics Subject Classification ID: 62-08
zbMATH DE Number: 6391631
zbMATH Keywords: model selection
zbMATH Keywords: model complexity
zbMATH Keywords: prediction error curves
zbMATH Keywords: time-to-event data
zbMATH Keywords: random survival forests
describes a project that uses: snowfall
describes a project that uses: ElemStatLearn
describes a project that uses: randomSurvivalForest
describes a project that uses: peperr
describes a project that uses: R
MaRDI profile type: Publication
full work available at URL: s00180-011-0236-6
OpenAlex ID: W2068241138
cites work: Adapting prediction error estimates for biased complexity selection in high-dimensional bootstrap samples
cites work: Random forests
cites work: Q3327527
cites work: Q5654899
cites work: Improvements on Cross-Validation: The .632+ Bootstrap Method
cites work: Efron‐Type Measures of Prediction Error for Survival Analysis
cites work: Strictly Proper Scoring Rules, Prediction, and Estimation
cites work: Random survival forests
cites work: High-Dimensional Variable Selection for Survival Data
DOI: 10.1007/S00180-011-0236-6
description: scientific article