Estimation of probability distribution for censored data

In this thesis, we look into estimation of probability distribution for censored data. These data are not complete, because for some reason it was impossible to observe them all. We use the Kaplan-Meier estimator and study some of its properties. We also use the Nelson-Aalen estimator. In the end we make a compa- rison of these estimators with a naive estimator, which omits the censored data. The comparison is illustrated on two numerical examples where we can see the main differences in the accuracy of the estimators. We will see that it is better to include the censored data to our estimations.