On estimation and influence diagnostics for a Bivariate Promotion Lifetime Model Based on the FGM Copula: A Fully Bayesian Computation

Authors

  • Adriano Kamimura Suzuki Universidade de São Paulo Departamento de Matemática Aplicada e Estatística do Instituto de Ciências Matemáticas e de Computação, SME - ICMC - USP, Av. Trabalhador Sancarlense, 400, 13566-590 São Carlos, SP, Brasil
  • Francisco Louzada Universidade de São Paulo
  • Vicente Garibay Cancho Universidade de São Paulo

DOI:

https://doi.org/10.5540/tema.2013.014.03.0441

Abstract

In this paper we propose a bivariate long-term model based on the Farlie-Gumbel-Morgenstern copula to model, where the marginals are assumed to be long-term promotion time structured. The proposed model allows for the presence of censored data and covariates. For inferential purpose a Bayesian approach via Markov Chain Monte Carlo is considered. Further, some discussions on the model selection criteria are given. In order to examine outlying and influential observations, we present a Bayesian case deletion influence diagnostics based on the Kullback-Leibler divergence. The newly developed procedures are illustrated on artificial and real data.

Author Biography

Adriano Kamimura Suzuki, Universidade de São Paulo Departamento de Matemática Aplicada e Estatística do Instituto de Ciências Matemáticas e de Computação, SME - ICMC - USP, Av. Trabalhador Sancarlense, 400, 13566-590 São Carlos, SP, Brasil

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Published

2013-11-24

How to Cite

Suzuki, A. K., Louzada, F., & Cancho, V. G. (2013). On estimation and influence diagnostics for a Bivariate Promotion Lifetime Model Based on the FGM Copula: A Fully Bayesian Computation. Trends in Computational and Applied Mathematics, 14(3), 441–461. https://doi.org/10.5540/tema.2013.014.03.0441

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Original Article