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In this paper we study a goodness-of-fit test based on the maximum correlation coefficient, in the context of randomly censored data. We construct a new test statistic under general right censoring and prove its asymptotic properties. 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, showing their advantages over the widely used Pearson-type test. Finally, we apply our test to the head-and-neck cancer data.
goodness-of-fit; kaplan-meier estimator; maximum correlation; random censoring