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Likelihood-based inference for bivariate latent failure time models with competing risks under the generalized FGM copula

Author

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  • Jia-Han Shih

    (National Central University)

  • Takeshi Emura

    (National Central University)

Abstract

Many existing latent failure time models for competing risks do not provide closed form expressions of sub-distribution functions. This paper suggests a generalized FGM copula models with the Burr III failure time distribution such that the sub-distribution functions have closed form expressions. Under the suggested model, we develop a likelihood-based inference method along with its computational tools and asymptotic theory. Based on the expressions of the sub-distribution functions, we propose goodness-of-fit tests. Simulations are conducted to examine the performance of the proposed methods. A real data from the reliability analysis of the radio transmitter-receivers are analyzed to illustrate the proposed methods. The computational programs are made available in the R package GFGM.copula.

Suggested Citation

  • Jia-Han Shih & Takeshi Emura, 2018. "Likelihood-based inference for bivariate latent failure time models with competing risks under the generalized FGM copula," Computational Statistics, Springer, vol. 33(3), pages 1293-1323, September.
    • Handle: RePEc:spr:compst:v:33:y:2018:i:3:d:10.1007_s00180-018-0804-0
    DOI: 10.1007/s00180-018-0804-0
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    References listed on IDEAS

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    Cited by:

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    2. Hirofumi Michimae & Takeshi Emura, 2022. "Likelihood Inference for Copula Models Based on Left-Truncated and Competing Risks Data from Field Studies," Mathematics, MDPI, vol. 10(13), pages 1-15, June.
    3. Šeliga Adam & Kauers Manuel & Saminger-Platz Susanne & Mesiar Radko & Kolesárová Anna & Klement Erich Peter, 2021. "Polynomial bivariate copulas of degree five: characterization and some particular inequalities," Dependence Modeling, De Gruyter, vol. 9(1), pages 13-42, January.
    4. 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.
    5. Prakash Chandra & Arvind Kumar Alok & Yogesh Mani Tripathi & Liang Wang, 2024. "Inference for A Generalized Family of Distributions Under Partially Observed Left Truncated and Right Censored Competing Risks Data," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 86(2), pages 809-844, November.
    6. 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.
    7. Kosuke Nakazono & Yu-Cheng Lin & Gen-Yih Liao & Ryuji Uozumi & Takeshi Emura, 2024. "Computation of the Mann–Whitney Effect under Parametric Survival Copula Models," Mathematics, MDPI, vol. 12(10), pages 1-22, May.
    8. Lo, Simon M.S. & Wilke, Ralf A. & Emura, Takeshi, 2024. "A semiparametric model for the cause-specific hazard under risk proportionality," Computational Statistics & Data Analysis, Elsevier, vol. 195(C).

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