  Asymptotic properties, both consistency and weak convergence, of estimators
arising in a general class of dynamic recurrent event models are presented. The
class of models take into account the impact of interventions after each event
occurrence, the impact of accumulating event occurrences, the induced
informative and dependent right-censoring mechanism due to the data-accrual
scheme, and the effect of covariate processes on the recurrent event
occurrences. The class of models subsumes as special cases many of the
recurrent event models that have been considered in biostatistics, reliability,
and in the social sciences. The asymptotic properties presented have the
potential of being useful in developing goodness-of-fit and model validation
procedures, confidence intervals and confidence bands constructions, and
hypothesis testing procedures for the finite- and infinite-dimensional
parameters of a general class of dynamic recurrent event models, albeit the
models without frailties.
