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A new generalized odd log-logistic flexible Weibull regression model with applications in repairable systems

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  • Prataviera, Fábio
  • Ortega, Edwin M.M.
  • Cordeiro, Gauss M.
  • Pescim, Rodrigo R.
  • Verssani, Bruna A.W.

Abstract

We define and study a four-parameter model called the generalized odd log-logistic flexible Weibull distribution. The new distribution can be used effectively in the context of reliability since it accommodates different hazard rate forms such as monotone, unimodal, bathtub-shaped, increasing-decreasing-increasing, among possible others. We provide an extensive study of the quantile function. Further, we present a parametric regression model based on the new distribution as an alternative to the location-scale regression model. An important property of this new regression model is that it does not need the assumption of proportional risks. We use the method of maximum likelihood for estimating the model parameters and perform various simulations for different parameter settings, sample sizes and censoring percentages. Applications in real engineering data sets illustrate the flexibility of the proposed models.

Suggested Citation

  • Prataviera, Fábio & Ortega, Edwin M.M. & Cordeiro, Gauss M. & Pescim, Rodrigo R. & Verssani, Bruna A.W., 2018. "A new generalized odd log-logistic flexible Weibull regression model with applications in repairable systems," Reliability Engineering and System Safety, Elsevier, vol. 176(C), pages 13-26.
    • Handle: RePEc:eee:reensy:v:176:y:2018:i:c:p:13-26
    DOI: 10.1016/j.ress.2018.03.034
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    References listed on IDEAS

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    1. Hashimoto, Elizabeth M. & Ortega, Edwin M.M. & Cancho, Vicente G. & Cordeiro, Gauss M., 2010. "The log-exponentiated Weibull regression model for interval-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 54(4), pages 1017-1035, April.
    2. Sangun Park & Jiwhan Park, 2018. "A general class of flexible Weibull distributions," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 47(4), pages 767-778, February.
    3. Alexander, Carol & Cordeiro, Gauss M. & Ortega, Edwin M.M. & Sarabia, José María, 2012. "Generalized beta-generated distributions," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1880-1897.
    4. Bebbington, Mark & Lai, Chin-Diew & Zitikis, RiÄ ardas, 2007. "A flexible Weibull extension," Reliability Engineering and System Safety, Elsevier, vol. 92(6), pages 719-726.
    5. Rodrigo R. Pescim & Edwin M. M. Ortega & Gauss M. Cordeiro & Morad Alizadeh, 2017. "A new log-location regression model: estimation, influence diagnostics and residual analysis," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(2), pages 233-252, January.
    6. Ayman Alzaatreh & Carl Lee & Felix Famoye, 2013. "A new method for generating families of continuous distributions," METRON, Springer;Sapienza Università di Roma, vol. 71(1), pages 63-79, June.
    7. Gauss M. Cordeiro & Giovana O. Silva & Edwin M. M. Ortega, 2016. "An extended-G geometric family," Journal of Statistical Distributions and Applications, Springer, vol. 3(1), pages 1-16, December.
    8. Giovana O. Silva & Edwin M.M. Ortega & Gilberto A. Paula, 2011. "Residuals for log-Burr XII regression models in survival analysis," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(7), pages 1435-1445, June.
    9. Krivtsov, Vasiliy V., 2007. "Practical extensions to NHPP application in repairable system reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 92(5), pages 560-562.
    10. Ahmed Z. Afify & Haitham M. Yousof & Gauss M. Cordeiro & Edwin M. M. Ortega & Zohdy M. Nofal, 2016. "The Weibull Fréchet distribution and its applications," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(14), pages 2608-2626, October.
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    Cited by:

    1. Peña-Ramírez, Fernando A. & Guerra, Renata Rojas & Canterle, Diego Ramos & Cordeiro, Gauss M., 2020. "The logistic Nadarajah–Haghighi distribution and its associated regression model for reliability applications," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    2. Freddy Hernández & Viviana Giampaoli, 2018. "The Impact of Misspecified Random Effect Distribution in a Weibull Regression Mixed Model," Stats, MDPI, vol. 1(1), pages 1-29, May.
    3. José H. Dias Gonçalves & João J. Ferreira Gomes & Lihki Rubio & Filipe R. Ramos, 2023. "A Generalized Log Gamma Approach: Theoretical Contributions and an Application to Companies’ Life Expectancy," Mathematics, MDPI, vol. 11(23), pages 1-23, November.
    4. Abba, Badamasi & Wang, Hong & Bakouch, Hassan S., 2022. "A reliability and survival model for one and two failure modes system with applications to complete and censored datasets," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    5. Ahmad, Abd EL-Baset A. & Ghazal, M.G.M., 2020. "Exponentiated additive Weibull distribution," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    6. Badamasi Abba & Hong Wang, 2024. "A new failure times model for one and two failure modes system: A Bayesian study with Hamiltonian Monte Carlo simulation," Journal of Risk and Reliability, , vol. 238(2), pages 304-323, April.
    7. Ducros, Florence & Pamphile, Patrick, 2018. "Bayesian estimation of Weibull mixture in heavily censored data setting," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 453-462.
    8. Shakhatreh, Mohammed K. & Lemonte, Artur J. & Moreno–Arenas, Germán, 2019. "The log-normal modified Weibull distribution and its reliability implications," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 6-22.
    9. Baker, Rose, 2019. "New survival distributions that quantify the gain from eliminating flawed components," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 493-501.

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