An information ratio‐based goodness‐of‐fit test for copula models on censored data

Tao Sun, Yu Cheng and Ying Ding

Biometrics, 2023, vol. 79, issue 3, 1713-1725

Abstract: Copula is a popular method for modeling the dependence among marginal distributions in multivariate censored data. As many copula models are available, it is essential to check if the chosen copula model fits the data well for analysis. Existing approaches to testing the fitness of copula models are mainly for complete or right‐censored data. No formal goodness‐of‐fit (GOF) test exists for interval‐censored or recurrent events data. We develop a general GOF test for copula‐based survival models using the information ratio (IR) to address this research gap. It can be applied to any copula family with a parametric form, such as the frequently used Archimedean, Gaussian, and D‐vine families. The test statistic is easy to calculate, and the test procedure is straightforward to implement. We establish the asymptotic properties of the test statistic. The simulation results show that the proposed test controls the type‐I error well and achieves adequate power when the dependence strength is moderate to high. Finally, we apply our method to test various copula models in analyzing multiple real datasets. Our method consistently separates different copula models for all these datasets in terms of model fitness.

Date: 2023
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