Likelihood-based inference for a frailty-copula model based on competing risks failure time data

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

7 Scopus Citations
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Author(s)

  • Yin-Chen Wang
  • Takeshi Emura
  • Tsai-Hung Fan
  • Simon M.S. Lo
  • Ralf Andreas Wilke

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)1622-1638
Journal / PublicationQuality and Reliability Engineering International
Volume36
Issue number5
Online published11 May 2020
Publication statusPublished - Jul 2020

Abstract

A competing risks phenomenon arises in industrial life tests, where multiple types of failure determine the working duration of a unit. To model dependence among marginal failure times, copula models and frailty models have been developed for competing risks failure time data. In this paper, we propose a frailty-copula model, which is a hybrid model including both a frailty term (for heterogeneity among units) and a copula function (for dependence between failure times). We focus on models that are useful to investigate the reliability of marginal failure times that are Weibull distributed. Furthermore, we develop likelihood-based inference methods based on competing risks data, including accelerated failure time models. We also develop a model-diagnostic procedure to assess the adequacy of the proposed model to a given dataset. Simulations are conducted to demonstrate the operational performance of the proposed methods, and a real dataset is analyzed for illustration. We make an R package “gammaGumbel” such that users can apply the suggested statistical methods to their data.

Research Area(s)

  • competing risk, copula, frailty, reliability, Weibull distribution

Citation Format(s)

Likelihood-based inference for a frailty-copula model based on competing risks failure time data. / Wang, Yin-Chen; Emura, Takeshi ; Fan, Tsai-Hung; Lo, Simon M.S.; Wilke, Ralf Andreas.

In: Quality and Reliability Engineering International, Vol. 36, No. 5, 07.2020, p. 1622-1638.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review