Real lifetime data application shows the flexibility of the proposed distribution and its better fit as compared to some existing models.
📌 3 Maddede Bu Makale
🎯
Amaç & Hipotez:This paper introduces a new generalization of Transmuted Power Function distribution named as Exponentiated Transmuted Power Function distribution with its fundamental properties.
📊
Temel Bulgular & Veri:The expressions of failure and survival rate functions on the basis of their graphs are provided.
💡
Sonuç & Katkı:Real lifetime data application shows the flexibility of the proposed distribution and its better fit as compared to some existing models.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Transmuted power function distribution · power function distribution · Rényi entropy
Abstract
This paper introduces a new generalization of Transmuted Power Function distribution named as Exponentiated Transmuted Power Function distribution with its fundamental properties. The expressions of failure and survival rate functions on the basis of their graphs are provided. We compute moments, moment generating function, quantile function. Then, Rényi entropy is discussed and the expressions of the order statistics are derived. Parameters of the proposed distribution are estimated using the maximum likelihood method. Real lifetime data application shows the flexibility of the proposed distribution and its better fit as compared to some existing models.
USMAN, R., HAQ, M. A. U., BURSA, N., ÖZEL, G. (2018). Exponentiated Transmuted Power Function Distribution: Theory & Applications. Gazi University Journal of Science, Cilt: 31 - Sayı: 2, 660–675. https://ajindex.com/articles/667183
Ahsanullah, M., & Kabir, A. L. (1974). A characterization of the power function distribution. The Canadian Journal of Statistics/La Revue Canadienne de Statistique, 95-98.
Ahsanullah, M. (1989). Estimation of the parameters of a power function distribution by record values. Pakistan Journal of Statistics, 5(2), 189-194.
Ali, M. M., & Woo, J. (2005). Inference on reliability P (Y Forbes, C., Evans, M., Hastings, N., & Peacock, B. (2011). Statistical distributions: John Wiley & Sons. Haq, M., Usman, R., & Fateh, A. (2016a). A study on Kumaraswamy power function distribution. Submitted to Kuwait Journal of Science. Haq, M. A., Butt, N. S., Usman, R. M., & Fattah, A. A. (2016b). Transmuted power function distribution. Gazi University Journal of Science, 29(1), 177-185.
Johnson, R. A. (1994). Miller and Freund's Probability and Statistics for Engineers: Prentice Hall. Johnson, N. L., Kotz, S., & Balakrishnan, N. (1995). Continuous univariate distributions, vol. 2 of Wiley series in probability and mathematical statistics: applied probability and statistics: Wiley, New York. Meniconi, M., & Barry, D. (1996). The power function distribution: A useful and simple distribution to assess electrical component reliability. Microelectronics Reliability, 36(9), 1207-1212.
Merovci, F. (2016). Transmuted rayleigh distribution. Austrian Journal of Statistics, 42(1), 21-31.
Oguntunde, P., & Adejumo, O. (2014). The transmuted inverse exponential distribution. International Journal of Advanced Statistics and Probability, 3(1), 1-7.
Shakeel, M., ul Haq, M. A., Hussain, I., Abdulhamid, A. M., & Faisal, M. (2016). Comparison of two new robust parameter estimation methods for the power function distribution. PloS One, 11(8), 1-11.
Sultan, R., Sultan, H., & Ahmad, S. (2014). Bayesian analysis of power function distribution under double priors. Journal of Statistics Applications & Probability, 3(2), 239. Tahir, M., Alizadeh, M., Mansoor, M., Cordeiro, G. M., & Zubair, M. (2014). The Weibull-power function distribution with applications. Hacettepe Journal of Mathematics and Statistics. Zaka, A., & Akhter, A. S. (2013). Methods for estimating the parameters of the power function distribution. Pakistan Journal of Statistics and Operation Research, 9(2), 1-12.