Tree-structured survival analysis.

By L. Gordon, Richard A. Olshen. 1985

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Citations in the corpus

Generalized random forests (2016, methodology): “(2011), Hall and Horowitz (2005), Newey and Powell (2003), and Su et al. (2013); see Newey (2013) for a review.”
Mining Electronic Health Records: A Survey (2017, methodology): “Research has also been carried out on extending decision trees to handle censored data [Gordon and Olshen 1985].”
Tree-based multivariate regression and density estimation with right-censored data (2004, methodology): “Included in the first category are: Breiman [1,2], Davis and Anderson [6], Gordon and Olshen [10], and LeBlanc and Crowley [16].”
Fifty Years of Classification and Regression Trees (2014, methodology): “Gordon & Olshen (1985) extended CART to censored response variables by fitting a Kaplan– Meier survival curve to the data in each node and using as node impurity the minimum Wasserstein distance between the fitted Kaplan–Meier curve and a point-mass function.”
Tree-augmented Cox proportional hazards models. (2005, background): “Gordon and Olshen (1985) , Ciapmi et al. (1986), Segal (1988), Davis and Anderson (1989),”
Unified Cross-Validation Methodology For Selection Among Estimators and a General Cross-Validated Adaptive Epsilon-Net Estimator: Finite Sample Oracle Inequalities and Examples (2003, unknown): “Examples of such approaches are provided in Gordon and Olshen (1985), Davis and Anderson (1989), and Leblanc and Crowley (1992).”
A review of survival trees (2011, methodology): “However, the first paper containing all the elements of what would become survival trees was written by Gordon and Olshen (1985).”
Machine Learning for Survival Analysis: A Survey (2017, methodology): “The authors in [Gordon and Olshen 1985] measured the homogeneity and Hellinger distances between the estimated distribution functions using the Wasserstein metric .”
Machine Learning for Survival Analysis (2019, methodology): “For example, Gordon and Olshen (1985) measured the homogeneity and Hellinger distances between the estimated distribution functions using the Wasserstein metric, while an exponential log-likelihood function was employed in Davis and Anderson (1989) for recursive partitioning based on the sum of…”