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In this paper, we introduce new parametric and semiparametric regressiontechniques for a recurrent event process subject to random right censoring. Wedevelop models for the cumula- tive mean function and provide asymptoticallynormal estimators. Our semiparametric model which relies on a single-indexassumption can be seen as a dimension reduction technique that, contrary to afully nonparametric approach, is not stroke by the curse of dimensional- itywhen the number of covariates is high. We discuss data-driven techniques tochoose the parameters involved in the estimation procedures and provide asimulation study to support our theoretical results.

Semiparametric inference for the recurrent event process by means of a single-index model