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.
|Date of creation:||Aug 2004|
|Date of revision:||Oct 2004|
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- Goncalves, Silvia & White, Halbert, 2002.
"Maximum Likelihood and the Bootstrap for Nonlinear Dynamic Models,"
University of California at San Diego, Economics Working Paper Series
qt8hx21540, Department of Economics, UC San Diego.
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