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Title: A note on nonparametric survival functions under censored and truncated data Authors:  Marialuisa Restaino - University of Salerno (Italy) [presenting]
Sara Milito - University of Salerno (Italy)
Abstract: Survival data (univariate and bivariate) have received considerable attention recently. In survival analysis, it is common to deal with incomplete information of the data, due to random censoring and random truncation. Most of the existing research on bivariate survival analysis focuses on considering the case when components are either censored or truncation or when one component is censored and truncated, but the other one is fully observed. Starting from this background, we will review the most used estimators for the survival function (univariate and bivariate), by taking into the incomplete information due to censoring and truncation. We will study the differences between them and we will compare their performance through a simulation study and application to real datasets.