Abstract
In previous chapters we considered modeling techniques that assume the predictor of the model to be an additive function of the covariates. Although this assumption is intuitive and facilitates interpretation of the models, it may happen that additive predictors do not capture the true data structure. This is, for example, the case when interactions between categorical covariates are present. In this chapter we consider recursive partitioning techniques (also termed “tree-based methods”), which are a popular approach to estimate non-additive predictors. Starting with an introduction to recursive partitioning, we consider two strategies to adapt tree-based methods to discrete survival data. The two strategies, which are described in detail in Sects. 6.2 and 6.3, are characterized by different choices for the split criterion to form the nodes of the trees. Section 6.4 contains an overview of tree ensembles for discrete survival data, which are designed to reduce the variance and to increase the prediction accuracy of single-tree methods.
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Tutz, G., Schmid, M. (2016). Tree-Based Approaches. In: Modeling Discrete Time-to-Event Data. Springer Series in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-28158-2_6
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DOI: https://doi.org/10.1007/978-3-319-28158-2_6
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