  Recurrence data arise from multi-disciplinary domains spanning reliability,
cyber security, healthcare, online retailing, etc. This paper investigates an
additive-tree-based approach, known as Boost-R (Boosting for Recurrence Data),
for recurrent event data with both static and dynamic features. Boost-R
constructs an ensemble of gradient boosted additive trees to estimate the
cumulative intensity function of the recurrent event process, where a new tree
is added to the ensemble by minimizing the regularized L2 distance between the
observed and predicted cumulative intensity. Unlike conventional regression
trees, a time-dependent function is constructed by Boost-R on each tree leaf.
The sum of these functions, from multiple trees, yields the ensemble estimator
of the cumulative intensity. The divide-and-conquer nature of tree-based
methods is appealing when hidden sub-populations exist within a heterogeneous
population. The non-parametric nature of regression trees helps to avoid
parametric assumptions on the complex interactions between event processes and
features. Critical insights and advantages of Boost-R are investigated through
comprehensive numerical examples. Datasets and computer code of Boost-R are
made available on GitHub. To our best knowledge, Boost-R is the first gradient
boosted additive-tree-based approach for modeling large-scale recurrent event
data with both static and dynamic feature information.
