Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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The Bivariate Kumaraswamy Weibull regression model: a complete classical and Bayesian analysis

  • Fachini-Gomes, Juliana B. (Department of Statistics, University of Brasilia) ;
  • Ortega, Edwin M.M. (Department of Exact Sciences, University of Sao Paulo) ;
  • Cordeiro, Gauss M. (Department of Statistics, Federal University of Pernambuco) ;
  • Suzuki, Adriano K. (Department of Applied Mathematics and Statistics, University of Sao Paulo)
  • Received : 2018.03.20
  • Accepted : 2018.08.14
  • Published : 2018.09.30
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Abstract

Bivariate distributions play a fundamental role in survival and reliability studies. We consider a regression model for bivariate survival times under right-censored based on the bivariate Kumaraswamy Weibull (Cordeiro et al., Journal of the Franklin Institute, 347, 1399-1429, 2010) distribution to model the dependence of bivariate survival data. We describe some structural properties of the marginal distributions. The method of maximum likelihood and a Bayesian procedure are adopted to estimate the model parameters. We use diagnostic measures based on the local influence and Bayesian case influence diagnostics to detect influential observations in the new model. We also show that the estimates in the bivariate Kumaraswamy Weibull regression model are robust to deal with the presence of outliers in the data. In addition, we use some measures of goodness-of-fit to evaluate the bivariate Kumaraswamy Weibull regression model. The methodology is illustrated by means of a real lifetime data set for kidney patients.

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References

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