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Fitting competing risks data to bivariate Pareto models

Author

Listed:
  • Jia-Han Shih
  • Wei Lee
  • Li-Hsien Sun
  • Takeshi Emura

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.

Suggested Citation

  • Jia-Han Shih & Wei Lee & Li-Hsien Sun & Takeshi Emura, 2019. "Fitting competing risks data to bivariate Pareto models," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 48(5), pages 1193-1220, March.
    • Handle: RePEc:taf:lstaxx:v:48:y:2019:i:5:p:1193-1220
    DOI: 10.1080/03610926.2018.1425450
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    Cited by:

    1. Nanami Taketomi & Kazuki Yamamoto & Christophe Chesneau & Takeshi Emura, 2022. "Parametric Distributions for Survival and Reliability Analyses, a Review and Historical Sketch," Mathematics, MDPI, vol. 10(20), pages 1-23, October.
    2. Zhiyuan Zuo & Liang Wang & Yuhlong Lio, 2022. "Reliability Estimation for Dependent Left-Truncated and Right-Censored Competing Risks Data with Illustrations," Energies, MDPI, vol. 16(1), pages 1-25, December.
    3. Emura, Takeshi & Lai, Ching-Chieh & Sun, Li-Hsien, 2023. "Change point estimation under a copula-based Markov chain model for binomial time series," Econometrics and Statistics, Elsevier, vol. 28(C), pages 120-137.

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