Goodness-of-fit test for randomly censored data based on maximum correlation

In this paper we study the goodness-of-fit test introduced by Fortiana and Grané (2003) and Grané (2012), in the context of randomly censored data. We construct a new test statistic undergeneral right-censoring, i.e., with unknown censoring distribution, and prove its asymptoticproperties. Additionally, we study a special case, when the censoring mechanism follows the well-known Koziol-Green model. We present an extensive simulation study on the empirical power of these two versions of the test statistic. We show the good performance of the test statistics in detecting symmetrical alternatives and their advantages over the most famousPearson-type test proposed by Akritas (1988). Finally, we apply our test to the head-and-neck-cancer data