Semiparametric inference for the recurrent event process by means of a single-index model
Résumé
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.
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BouazizGeffrayLopezSup.pdf (163.2 Ko)
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BouazizGeffrayLopez.pdf (319.67 Ko)
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