In modeling censored data, survival forest models are a competitive nonparametric alternative to traditional parametric or semiparametric models when the function forms are possibly misspecified or the underlying assumptions are violated. In this work, we propose a survival forest approach with trees constructed using a novel pseudo R2 splitting rules. By studying the well-known benchmark data sets, we find that the proposed model generally outperforms popular survival models such as random survival forest with different splitting rules, Cox proportional hazard model, and generalized boosted model in terms of C-index metric.
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