B1475
Title: Nonparametric distribution function estimation and goodness-of-fit testing
Authors: James Allison - North-West University (South Africa) [presenting]
Jaco Visagie - North-West University (South Africa)
Elzanie Bothma - North-West University (South Africa)
Abstract: When analysing lifetime data in the presence of censoring, one is often required to estimate the distribution function of the lifetimes non-parametrically. The most popular estimator used for this purpose is the Kaplan-Meier estimator. For values larger than the sample maximum, two different assumptions are commonly used for this estimator in the statistical literature. The first is to set the value of the estimate to one, while the second is to use the value of the estimate at the sample maximum when estimating the tail of the distribution function. We illustrate the profound effect of these assumptions on the sizes and powers of goodness-of-fit tests in both the i.i.d. case and mixture cure model.
