High dimensional variable selection with clustered data: an application of random multivariate survival forests for detection of outlier medical device components

DOI10.1080/00949655.2019.1584198OpenAlexW2917383341WikidataQ128308414 ScholiaQ128308414MaRDI QIDQ5107399

Juanjuan Fan, Peter Calhoun, Guy Cafri

Publication date: 27 April 2020

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

Full work available at URL: https://doi.org/10.1080/00949655.2019.1584198

zbMATH Keywords

survivalstratificationmultivariaterandom forestmedical devices

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• randomSurvivalForest

Cites Work

• Unnamed Item
• Random survival forests
• Power loss of stratified log-rank test in homogeneous samples
• Multivariate exponential survival trees and their application to tooth prognosis
• Variable importance in binary regression trees and forests
• Multivariate Survival Trees: A Maximum Likelihood Approach Based on Frailty Models
• Separating Between- and Within-Cluster Covariate Effects by Using Conditional and Partitioning Methods
• Biased estimates of treatment effect in randomized experiments with nonlinear regressions and omitted covariates
• A nonidentifiability aspect of the problem of competing risks.
• Between- and Within-Cluster Covariate Effects in the Analysis of Clustered Data
• High-Dimensional Variable Selection for Survival Data
• Trees for Correlated Survival Data by Goodness of Split, With Applications to Tooth Prognosis
• Random forests

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