  This review article provides an overview of recent work in the modeling and
analysis of recurrent events arising in engineering, reliability, public
health, biomedicine and other areas. Recurrent event modeling possesses unique
facets making it different and more difficult to handle than single event
settings. For instance, the impact of an increasing number of event occurrences
needs to be taken into account, the effects of covariates should be considered,
potential association among the interevent times within a unit cannot be
ignored, and the effects of performed interventions after each event occurrence
need to be factored in. A recent general class of models for recurrent events
which simultaneously accommodates these aspects is described. Statistical
inference methods for this class of models are presented and illustrated
through applications to real data sets. Some existing open research problems
are described.
