A k-sample test with interval censored data
1 Department of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam Road, Hong Kong. kcyuen{at}hku.hk, 2 Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China. jshi{at}iss.ac.cn, 3 Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong. lzhu{at}hkbu.edu.hk
The problem of testing for the equality of k distribution functions under Case 2 interval censoring is studied and a supremum-type test statistic is proposed based on the differences between the nonparametric maximum likelihood estimator and the so-called leveraged bootstrap estimator of the k underlying distributions. The proposed test is distributionfree and consistent against all alternatives. As the main results hold for a wide range of resampling sizes, a data-driven method is suggested for determining the size of each leveraged bootstrap sample. Another advantage of the test is that it can detect different distributions with equal means or heavy crossover. Simulation studies indicate that the test performs quite well with a moderate sample size. Finally, a slightly modified version of the test is applied to breast cosmesis data.
Key Words: Goodness of fit; Interval censoring; Iterative convex minorant algorithm; Leveraged bootstrap; Nonparametric maximum likelihood.
Received January 2005. Revised October 2005.