Spline regression for hazard rate estimation when data are censored and measured with error
Résumé
In this paper, we study an estimation problem where the variables of interest are subject to both right censoring and measurement error. In this context, we propose a nonparametric estimation strategy of the hazard rate, based on a regression contrast minimized in a finite dimensional functional space generated by splines bases. We prove a risk bound of the estimator in term of integrated mean square error, discuss the rate of convergence when the dimension of the projection space is adequately chosen. Then, we define a data driven criterion of model selection and prove that the resulting estimator performs an adequate compromise. The method is illustrated via simulation experiments which prove that the strategy is successful.
Domaines
Statistiques [math.ST]| Origine | Fichiers produits par l'(les) auteur(s) |
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