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

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

2010-05-25

arxiv.org/abs/1005.4553v2

Mathematics

Statistics Theory

Scientific paper

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 cumulative mean function and provide asymptotically normal estimators. Our semiparametric model which relies on a single-index assumption can be seen as a reduction dimension technique that, contrary to a fully nonparametric approach, is not stroke by the curse of dimensionality 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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