Reproduce “Random Jump Forests” on OpenResearch
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About this paper
Martin Bladt, Rasmus Frigaard Lemvig
Random jump forests estimate multi-state occupation probabilities by learning shared covariate partitions across transitions and averaging Nelson–Aalen rate estimates across trees. In simulations with time-varying effects, median relative IBS differences favored JF by 14.2–15.8% over Cox and 6.9–8.9% over Cox with interactions for n ≥ 2,000. Theory assumes finite-valued covariates; pooling can undermine confidence-band coverage.
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