A Model Selection Test for Bivariate Failure-Time Data
In this paper, we address two important issues in survival model selection for censored data generated by the Archimedean copula family; method of estimating the parametric copulas and data reuse. We demonstrate that for model selection, estimators of the parametric copulas based on minimizing the selection criterion function may be preferred to other estimators. To handle the issue of data reuse, we put model selection in the context of hypothesis testing and propose a simple test for model selection from a finite number of parametric copulas. Results from a simulation study and two empirical applications provide strong support to our theoretical findings.