Title: Boosted nonparametric hazards with time-dependent covariates,
Abstract: Given functional data samples from a survival process with time dependent
covariates, we propose a practical boosting procedure for estimating its hazard
function nonparametrically. The estimator is consistent if the model is
correctly specified; alternatively an oracle inequality can be demonstrated for
tree-based models. To avoid overfitting, boosting employs several
regularization devices. One of them is step-size restriction, but the rationale
for this is somewhat mysterious from the viewpoint of consistency. Our
convergence bounds bring some clarity to this issue by revealing that step-size
restriction is a mechanism for preventing the curvature of the risk from
derailing convergence. We use our boosting procedure to shed new light on a
question from the operations literature concerning the effect of workload on
service rates in an emergency department.