In this paper, we introduce new parametric and semiparametric regression
techniques for a recurrent event process subject to random right censoring. We
develop models for the cumula- tive mean function and provide asymptotically
normal estimators. Our semiparametric model which relies on a single-index
assumption can be seen as a dimension reduction technique that, contrary to a
fully nonparametric approach, is not stroke by the curse of dimensional- ity
when the number of covariates is high. We discuss data-driven techniques to
choose the parameters involved in the estimation procedures and provide a
simulation study to support our theoretical results
