Resampling Methods for Testing a Semiparametric Random Censorship Model

Abstract

This paper presents a goodness‐of‐fit test for a semiparametric random censorship model proposed by Dikta (1998). The test statistic is derived from a model‐based process which is asymptotically Gaussian. In addition to test consistency, the proposed test can detect local alternatives distinct n‐1/2 from the null hypothesis. Due to the intractability of the asymptotic null distribution of the test statistic, we turn to two resampling approximations. We first use the well‐known bootstrap method to approximate critical values of the test. We then introduce a so‐called random symmetrization method for carrying out the test. Both methods perform very well with a sample of moderate size. A simulation study shows that the latter possesses better empirical powers and sizes for small samples.