Etude de modèles semi et non paramétriques pour des données censurées
MetadataShow full item record
In this dissertation, we are interested in kernel estimation of the density and the failure rate for c ensored data. There exist seve ral kinds of censorship and we fo cus on right, doubly and twi ce censored data mo de ls. We consider a general framework of censorship, including all these mo dels, and we prove a result on the asymptotic normality of a kernel de nsi ty estimator that we intro duce. This result allows us to deduce the asymptotic normality of the density and failure rate e sti mates for the ab ove-mentioned censorship mo dels. We also establish the mean square convergence, with rates, of the same estimators in the case of twice censored data. In a second part of the dissertation, we study semiparametric mo dels which verify linear constraints involving an unknown parameter. We assume that the variable of interest is right c ensored and we use the theory of divergences to construct estimates for the pa ram eter of interest. Simulation studies are presented in order to illustrate the p erformances of the di erent studied estim ators.
- Doctorat (Mathématiques)