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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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https://hal.archives-ouvertes.fr/hal-00446528
Contributor : Olivier Bouaziz <>
Submitted on : Tuesday, September 16, 2014 - 6:23:01 PM
Last modification on : Saturday, April 11, 2020 - 2:01:07 AM
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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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