Abstract:
In this thesis, we are interested in the nonparametric estimation of the conditional density and the conditional mode, in a twice censorship model. This means that the response variable Y is right censored by a variable R and that min(Y, R) is left censored. On the one hand, we build estimators, by the kernel method, for the conditional density and the conditional mode and establish a rate of the almost complete convergence for them. On the other hand, we give the rate of the mean square convergence of the conditional density estimator. Finally, a simulation study is conducted in order to illustrate, for a nite size, the performance of the proposed estimators.
