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

Abstract : 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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Statistics A Journal of Theoretical and Applied Statistics, 2015, 49 (2), pp.361-385. <10.1080/02331888.2014.929134>
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Olivier Bouaziz, Ségolen Geffray, Olivier Lopez. Semiparametric inference for the recurrent event process by means of a single-index model. Statistics A Journal of Theoretical and Applied Statistics, 2015, 49 (2), pp.361-385. <10.1080/02331888.2014.929134>. <hal-00446528v3>

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