A Note on Breiman's Random Forest Data Mining Technique and Conventional Cox Modeling of Survival Statistics: The Case of the Phantom “Induct” Covariate in the Ohio State University Kidney Transplant Database

DOI10.1080/03610920601126431zbMath1315.62086OpenAlexW2121541237MaRDI QIDQ5421549

George T. Diderrich, Ronald P. Pelletier

Publication date: 24 October 2007

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610920601126431

zbMATH Keywords

data miningimportance of variablesCox proportional hazard modelBreiman random forestskidney transplant dataobservational data analysissurvival statistics

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Learning and adaptive systems in artificial intelligence (68T05)

Uses Software

• ElemStatLearn

Cites Work

• Survival analysis. Techniques for censored and truncated data
• The elements of statistical learning. Data mining, inference, and prediction
• Random forests

This page was built for publication: A Note on Breiman's Random Forest Data Mining Technique and Conventional Cox Modeling of Survival Statistics: The Case of the Phantom “Induct” Covariate in the Ohio State University Kidney Transplant Database