Oblique random survival forests
DOI10.1214/19-AOAS1261zbMath1433.62305OpenAlexW2980392895MaRDI QIDQ2281237
Mario Sims, D. Leann Long, Yuan-I Min, Jeff M. Szychowski, Byron C. Jaeger, Leslie A. McClure, Dustin M. Long, George Howard, Noah Robin Simon
Publication date: 19 December 2019
Published in: The Annals of Applied Statistics (Search for Journal in Brave)
Full work available at URL: https://projecteuclid.org/euclid.aoas/1571277776
Computational methods for problems pertaining to statistics (62-08) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Estimation in survival analysis and censored data (62N02)
Related Items (2)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Greedy function approximation: A gradient boosting machine.
- Random survival forests
- Hierarchical Clustering With Prototypes via Minimax Linkage
- Boosting ridge regression
- Double-bagging: Combining classifiers by bootstrap aggregation
- The asymptotic theory of permutation statistics.
- Least angle regression. (With discussion)
- A review of survival trees
- Oblique random survival forests
- Statistical Analysis of Financial Data in S-Plus
- Frozen robust multiarray analysis (fRMA)
- Regression Trees for Censored Data
- Approximations of the critical region of the fbietkan statistic
- Time‐Dependent ROC Curves for Censored Survival Data and a Diagnostic Marker
- Identification of biomarker‐by‐treatment interactions in randomized clinical trials with survival outcomes and high‐dimensional spaces
- Regularization and Variable Selection Via the Elastic Net
- Reinforcement Learning Trees
- Survival Model Predictive Accuracy and ROC Curves
- Statistical models based on counting processes
- Extremely randomized trees
- The elements of statistical learning. Data mining, inference, and prediction
- Random forests
This page was built for publication: Oblique random survival forests
