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

TL;DR

This paper develops new semiparametric regression methods for recurrent event data with right censoring, using a single-index model to reduce dimensionality and improve estimation in high-dimensional settings.

Contribution

It introduces a novel single-index semiparametric model for recurrent event processes, addressing high-dimensional covariates and providing asymptotic properties and data-driven parameter selection.

Findings

Estimation procedures are asymptotically normal.

Simulation studies support theoretical results.

Model effectively handles high-dimensional covariates.

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.