Censoring Unbiased Regression Trees and Ensembles

This article proposes a novel paradigm for building regression trees and ensemble learning in survival analysis. Generalizations of the classification and regression trees (CART) and random forests (RF) algorithms for general loss functions, and in the latter case more general bootstrap procedures,...

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Bibliographic Details
Published in:Journal of the American Statistical Association 2019-01, Vol.114 (525), p.370-383
Main Authors: Steingrimsson, Jon Arni, Diao, Liqun, Strawderman, Robert L.
Format: Article
Language:English
Subjects:
Algorithms
Classification
Classification and regression trees (CART)
Computer simulation
Doubly robust
Forests
Generalizations
Learning
Loss estimation
Random forests
Regression analysis
Risk prediction
Robustness (mathematics)
Statistical methods
Statistics
Survival
Survival analysis
Theory and Methods
Trees
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Summary:This article proposes a novel paradigm for building regression trees and ensemble learning in survival analysis. Generalizations of the classification and regression trees (CART) and random forests (RF) algorithms for general loss functions, and in the latter case more general bootstrap procedures, are both introduced. These results, in combination with an extension of the theory of censoring unbiased transformations (CUTs) applicable to loss functions, underpin the development of two new classes of algorithms for constructing survival trees and survival forests: censoring unbiased regression trees and censoring unbiased regression ensembles. For a certain "doubly robust" CUT of squared error loss, we further show how these new algorithms can be implemented using existing software (e.g., CART, RF). Comparisons of these methods to existing ensemble procedures for predicting survival probabilities are provided in both simulated settings and through applications to four datasets. It is shown that these new methods either improve upon, or remain competitive with, existing implementations of random survival forests, conditional inference forests, and recursively imputed survival trees.
ISSN:0162-1459
1537-274X
DOI:10.1080/01621459.2017.1407775