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Log-Burr XII Gamma–Weibull Regression Model with Random Effects and Censored Data

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Abstract

It may happen in some applications that the assumption of independence of survival times does not hold. Thus, we propose a new log-Burr XII regression model with log-gamma–Weibull distributions for the random effects. The maximum likelihood method is used to estimate the model parameters based on the Gauss–Hermite numerical integration technique. For different parameter settings, sample sizes, censoring percentages and correlated data, various simulations are performed. Some global-influence measurements are also investigated. In order to assess the robustness of the maximum likelihood estimators, we evaluate local influence on the estimates of the parameters under different perturbation schemes. We illustrate the importance of the new model by means of a real data set in analysis of experiments.

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Acknowledgements

We thank two referees, the Associate Editor and the Editor for all their suggestions and comments to improve the manuscript. This study was funded by the Foundation for State of São Paulo (FAPESP) (Process No 2010/04496-2) and CNPq, Brazil.

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Authors and Affiliations

  1. Departamento Acadêmico de Matemática, UTFPR, Curitiba, Brazil

    Elizabeth M. Hashimoto

  2. Departamento de Estatística, UFBA, Salvador, Brazil

    Giovana O. Silva

  3. Departamento de Ciências Exatas, ESALQ - USP, Av. Pádua Dias 11, Caixa Postal 9, 13418-900, Piracicaba, São Paulo, Brazil

    Edwin M. M. Ortega

  4. Departamento de Estatística, UFPE, Recife, Brazil

    Gauss M. Cordeiro

Authors
  1. Elizabeth M. Hashimoto
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  2. Giovana O. Silva
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  3. Edwin M. M. Ortega
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  4. Gauss M. Cordeiro
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Corresponding author

Correspondence to Edwin M. M. Ortega.

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Hashimoto, E.M., Silva, G.O., Ortega, E.M.M. et al. Log-Burr XII Gamma–Weibull Regression Model with Random Effects and Censored Data. J Stat Theory Pract 13, 27 (2019). https://doi.org/10.1007/s42519-018-0026-3

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  • DOI: https://doi.org/10.1007/s42519-018-0026-3

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