In survival analysis it is often the case that a lifetime of interest is right-censored by a random censoring lifetime. Under right-censoring, if the censoring lifetime is larger than the lifetime of interest then the lifetime of interest is observed, and otherwise the censoring lifetime is observed. Accordingly, a right-censored sample consists of two complementary subsamples containing observations of the lifetime of interest and observations of the censoring lifetime. One of the aims of this chapter is to shed light on how these subsamples may be used for efficient estimation, and then get experience in statistical analysis of small samples with high rate of censoring. The content of the chapter is as follows. Introduction to the right-censoring can be found in Sect. 3.1. The classical problem of survival function estimation is considered in Sect. 3.2. Here the Kaplan-Meier and the method of moments estimators are discussed. Sect. 3.3 is devoted to density estimation. Hazard rate is another classical characteristic of a lifetime, and its nonparametric estimation is explained in Sect. 3.4. Nonparametric regression with right-censored response or right-censored predictor is explored in Sect. 3.5. Nonparametric estimation of conditional distributions is considered in Sect. 3.6. Interesting and practically important topic of estimating bivariate survival function and bivariate density is studied in Sect. 3.7. A classical in survival analysis problem of estimating the mean residual life and the bivariate mean residual life is considered in Sect. 3.8. Exercises are placed in Sect. 3.9, and the literature review and topics for future research can be found in the Notes.