Fitting competing risks data to bivariate Pareto models

ABSTRACT

This paper revisits two bivariate Pareto models for fitting competing risks data. The first model is the Frank copula model, and the second one is a bivariate Pareto model introduced by Sankaran and Nair (1993). We discuss the identifiability issues of these models and develop the maximum likelihood estimation procedures including their computational algorithms and model-diagnostic procedures. Simulations are conducted to examine the performance of the maximum likelihood estimation. Real data are analyzed for illustration.

KEYWORDS:

• Bivariate Pareto distribution
• Frank copula
• Kendall's tau
• Newton-Raphson algorithm
• Survival analysis.

Acknowledgements

The authors thank two anonymous reviewers for their helpful comments that improved the paper. This work is supported by the research grants funded by the government of Taiwan (MOST 105-2118-M-008-003-MY2).

Additional information

Funding

Ministry of Science and Technology, Taiwan MOST 105-2118-M-008-003-MY2.