In this paper, we provide a nonparametric estimator of the distribution of bivariate censored lifetimes, in a model where the two censoring variables differ only through an additional observed variable. This situation is motivated by a particular application to insurance, where the supplementary...

The problem of estimating a nonlinear regression model, when the dependent variable is randomly censored, is considered. The parameter of the model is estimated by least squares using synthetic data. Consistency and asymptotic normality of the least squares estimators are derived. The proofs are...

The problem of estimating a nonlinear regression model when the dependent variableis randomly censored is considered. The parameter of the model is estimated by leastsquares using synthetic data, that is a suitable transformation of the response variablesthat preserves the conditional...

We develop two kernel smoothing based tests of a parametric mean-regressionmodel against a nonparametric alternative when the response variable is right-censored. The new test statistics are inspired by the synthetic data and the weightedleast squares approaches for estimating the parameters of...

In this paper we provide a new nonparametric estimator of the joint distribution of two lifetimes under random right censoring and left truncation which can be seen as a bivariate extension of the Kaplan–Meier estimator. We derive asymptotic results for this estimator, including uniform...

In a regression model with univariate censored responses, a new estimatorof the joint distribution function of the covariates and response is proposed,under the assumption that the response and the censoring variable are independentconditionally to the covariates. This estimator is an extension...