Fitting competing risks data to bivariate Pareto models

  • Wei Lee (Contributor)
  • Li-Hsien Sun (Contributor)
  • Takeshi Emura (Contributor)
  • Jia Han Shih (Contributor)



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.
Date made available4 Mar 2019
Publisherfigshare Academic Research System

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