Fitting competing risks data to bivariate Pareto models

Jia Han Shih, Wei Lee, Li Hsien Sun, Takeshi Emura

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


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.

Original languageEnglish
Pages (from-to)1193-1220
Number of pages28
JournalCommunications in Statistics - Theory and Methods
Issue number5
StatePublished - 4 Mar 2019


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


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