References
- Afify, W. M. (2016). Adding a new parameter in flexible Weibull distribution using Marshall-Olkin model its application. Advances and Applications in Statistics, 48(2), 157–167. https://doi.org/https://doi.org/10.17654/AS048020157
- Ahmad, Z., & Iqbal, B. (2017). Generalized flexible Weibull extension distribution. Circulation in Computer Science, 2(4), 68–75. https://doi.org/https://doi.org/10.22632/ccs-2017-252-11
- Al Abbasi, J. N. (2016). Kumaraswamy inverse flexible Weibull distribution: Theory and application. International Journal of Computer Applications, 154, 41–46.
- Bebbington, M., Lai, C. D., & Zitikis, R. (2007). A flexible Weibull extension. Reliability Engineering & System Safety, 92(6), 719–726. https://doi.org/https://doi.org/10.1016/j.ress.2006.03.004
- Brooks, S. P. (2002). Discussion on the paper by Spiegelhalter, Best, Carlin, and van der Linde, 64, 616–618.
- Buuren, S. V., & Fredriks, M. (2001). Worm plot: A simple diagnostic device for modelling growth reference curves. Statistics in Medicine, 20(8), 1259–1277. https://doi.org/https://doi.org/10.1002/sim.746
- Carlin, B. P., & Louis, T. A. (2001). Bayes and empirical bayes methods for data analysis (2nd ed.). Chapman and Hall.
- Chib, S., & Greenberg, E. (1995). Understanding the metropolis-Hastings algorithm. The American Statistician, 49(4), 327–335. https://doi.org/https://doi.org/10.2307/2684568
- Cole, T. J., & Green, P. J. (1992). Smoothing reference centile curves: The LMS method and penalized likelihood. Statistics in Medicine, 11(10), 1305–1319. https://doi.org/https://doi.org/10.1002/sim.4780111005
- Cordeiro, G. M., Prataviera, F., do Carmo S Lima, M., & Ortega, E. M. (2019). The Marshall-Olkin extended flexible Weibull regression model for censored lifetime data. Model Assisted Statistics and Applications, 14(1), 1–17. https://doi.org/https://doi.org/10.3233/MAS-180455
- Cowles, M. K., & Carlin, B. P. (1996). Markov chain Monte Carlo convergence diagnostics: A comparative review. Journal of the American Statistical Association, 91(434), 883–904. https://doi.org/https://doi.org/10.1080/01621459.1996.10476956
- Dunn, P. K., & Smyth, G. K. (1996). Randomized quantile residuals. Journal of Computational and Graphical Statistics, 5, 236–244.
- El-Damcese, M. A., Mustafa, A., El-Desouky, B. S., & Mustafa, M. E. (2016). The Kumaraswamy flexible Weibull extension. International Journal of Mathematics and Its Applications, 7, 1–14.
- El-Desouky, B. S., Mustafa, A., & Al-Garash, S. (2017). The exponential FlexibleWeibull extension distribution. Open Journal of Modelling and Simulation, 05(01), 83–97. https://doi.org/https://doi.org/10.4236/ojmsi.2017.51007
- El-Gohary, A., El-Bassiouny, A. H., & El-Morshedy, M. (2015). Inverse flexible Weibull extension distribution. International Journal of Computer Applications, 115(2), 46–51. https://doi.org/https://doi.org/10.5120/20127-2211
- El-Morshedy, M., El-Bassiouny, A. H., & El-Gohary, A. (2017). Exponentiated inverse flexible Weibull extension distribution. Journal of Statistics Applications & Probability, 6(1), 169–183. https://doi.org/https://doi.org/10.18576/jsap/060114
- El-Morshedy, M., & Eliwa, M. S. (2019). The odd flexible Weibull-H family of distributions: Properties and estimation with applications to complete and upper record data. Filomat, 33(9), 2635–2652. https://doi.org/https://doi.org/10.2298/FIL1909635E
- Kenney, J. F., & Keeping, E. S. (1962). Kurtosis. Mathematics of Statistics, 3, 102–103.
- Khaleel, M. A., Oguntunde, P. E., Ahmed, M. T., Ibrahim, N. A., & Loh, Y. F. (2020). The Gompertz flexible Weibull distribution and its applications. Malaysian Journal of Mathematical Sciences, 14, 169–190.
- Lawless, J. F. (2003). Statistical models and methods for lifetime data. John Wiley and Sons.
- Louzada-Neto, F., Mazucheli, J., & Achcar, J. A. (2001). Uma introdução à análise de sobrevivência e confiabilidade. Sociedad Chilena de Estatística.
- Mitnik, P. A., & Baek, S. (2013). The Kumaraswamy distribution: Median-dispersion re-parameterizations for regression modeling and simulation-based estimation. Statistical Papers, 54(1), 177–192. https://doi.org/https://doi.org/10.1007/s00362-011-0417-y
- Moors, J. J. A. (1988). A quantile alternative for kurtosis. The Statistician, 37(1), 25–32. https://doi.org/https://doi.org/10.2307/2348376
- Mustafa, A., El-Desouky, B. S., & Shamsan, A. G. (2016). The Weibull generalized flexible Weibull extension distribution. Journal of Data Science, 14(3), 453–477. https://doi.org/https://doi.org/10.6339/JDS.201607_14(3).0004
- Mustafa, A., El-Desouky, B. S., & Shamsan, A. G. (2018). Odd generalized exponential flexible Weibull extension distribution. Journal of Statistical Theory and Applications, 17(1), 77–90. https://doi.org/https://doi.org/10.2991/jsta.2018.17.1.6
- Park, S., & Park, J. (2018). A general class of flexible Weibull distributions. Communications in Statistics - Theory and Methods, 47(4), 767–778. https://doi.org/https://doi.org/10.1080/03610926.2015.1118509
- Prataviera, F., Ortega, E. M., Cordeiro, G. M., Pescim, R. R., & Verssani, B. A. (2018). A new generalized odd log-logistic flexible Weibull regression model with applications in repairable systems. Reliability Engineering & System Safety, 176, 13–26. https://doi.org/https://doi.org/10.1016/j.ress.2018.03.034
- Plummer, M. (2003). JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling. Proceedings of the 3rd International Workshopon Distributed Statistical Computing.
- Rigby, R. A., & Stasinopoulos, D. M. (1996a). A semi-parametric additive model for variance heterogeneity. Statistics and Computing, 6(1), 57–65. https://doi.org/https://doi.org/10.1007/BF00161574
- Rigby, R. A., & Stasinopoulos, D. M. (1996b). Mean and dispersion additive models. In W. Hardle and M. G. Schimek (Eds.), Statistical theory and computational aspects of smoothing (pp. 215–230). Physica.
- Sánchez, L., Leiva, V., Galea, M., & Saulo, H. (2020). Birnbaum-Saunders quantile regression models with application to spatial data. Mathematics, 8(6), 1000. https://doi.org/https://doi.org/10.3390/math8061000
- Sánchez, L., Leiva, V., Galea, M., & Saulo, H. (2021). Birnbaum - Saunders quantile regression and its diagnostics with application to economic data. Applied Stochastic Models in Business and Industry, 37(1), 53–73. https://doi.org/https://doi.org/10.1002/asmb.2556
- Santos-Neto, M., Cysneiros, F. J. A., Leiva, V., & Barros, M. (2016). Reparameterized Birnbaum-Saunders regression models with varying precision. Electronic Journal of Statistics, 10(2), 2825–2855. https://doi.org/https://doi.org/10.1214/16-EJS1187
- Spiegelhalter, D. J., Best, N. G., Carlin, B. P., & Van der Linde, A. (2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64(4), 583–639. https://doi.org/https://doi.org/10.1111/1467-9868.00353
- Stasinopoulos, D. M., & Rigby, R. A. (2007). Generalized additive models for location scale and shape (GAMLSS) in R. Journal of Statistical Software, 23, 1–46.
- Stasinopoulos, D. M., Rigby, R. A., Heller, G. Z., Voudouris, V., & De Bastiani, F. (2017). Flexible regression and smoothing: Using GAMLSS in R. Chapman and Hall/CRC.
- Stone, G. C. (1978). Statistical analysis of accelerated aging tests on solid electrical insulation. Unpublished M.A.Sc. thesis, University of Waterloo, Canada.
- Suprawhardana, M. S., & Prayoto, S. (1999). Total time on test plot analysis for mechanical components of the RSG-GAS reactor. Atom Indonesia, 25, 81–90.