High-Dimensional Variable Selection for Survival Data

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Interpretable Machine Learning - A Brief History, State-of-the-Art and Challenges (2020, methodology): “For the random forest, the minimal depth distribution [85,55] and the Gini importance [13] analyze the structure of the trees of the forest and can be used to quantify feature importance.”
A random forest based approach for predicting spreads in the primary catastrophe bond market (2020, background): “These are those predictors having forest averaged minimal depth lower that the mean of the minimal depth distribution, namely mean minimal depth threshold according to Ishwaran et al. (2010), here denoted by T*.”
Tumor Interferon Signaling Regulates a Multigenic Resistance Program to Immune Checkpoint Blockade (2016, positive): “Because of high correlations between IFIT1/MX1with the multiple genes originally examined for IFN-driven resistance (Figures 7E and 3H), we used bootstrapping and previously described variable selection methods (Ishwaran et al., 2010) to better assess performance of IFIT1/MX1 and nsSNVs against thes”
Random Forests for Genomic Data Analysis (2012, background): “estimatedmodel size from the repetitions are selected for thefinalmodel [3,22].”
A review of survival trees (2011, methodology): “, 2008) while the third one, the minimal depth of a maximal subtree, does not (Ishwaran et al., 2010).”