journal article Open Access Dec 30, 2024

Distributional properties of the entropy transformed Weibull distribution and applications to various scientific fields

View at Publisher Save 10.1038/s41598-024-83132-w
Topics

No keywords indexed for this article. Browse by subject →

References
50
[1]
Liu, Y., Liu, C. & Wang, D. Understanding atmospheric behavior in terms of entropy: A review of applications of the second law of thermodynamics to meteorology. Entropy 13, 211–240. https://doi.org/10.3390/e13010211 (2011). 10.3390/e13010211
[2]
Seidenfeld, T. Entropy and uncertainty. Philosophy Sci. 53(4), 467–491. https://doi.org/10.1086/289336 (1986). 10.1086/289336
[3]
Brissaud, J. B. The meanings of entropy. Entropy 7(1), 68–96. https://doi.org/10.3390/e7010068 (2005). 10.3390/e7010068
[4]
Ou, J. Theory of portfolio and risk based on incremental entropy. J. Risk Finance 6(1), 31–39. https://doi.org/10.1108/15265940510574754 (2005). 10.1108/15265940510574754
[5]
Xu, J., Zhou, X. & Wu, D. D. Portfolio selection using λ mean and hybrid entropy. Ann. Oper. Res. 185, 213–229. https://doi.org/10.1007/s10479-009-0550-3 (2011). 10.1007/s10479-009-0550-3
[6]
Shaw, D. & Davis, C. H. Entropy and information: A multidisciplinary overview. J. Am. Soc. Inf. Sci. 34, 67–74. https://doi.org/10.1002/asi.4630340110 (1983). 10.1002/asi.4630340110
[7]
Flores, C. F., Ulloa Lugo, N. & Covarrubias Martínez, H. The concept of entropy, from its origins to teachers. Revista Mexicana de Física E 61(2), 69–80 (2015).
[8]
Zhou, R. X., Liu, S. C. & Qiu, W. H. Survey of applications of entropy in decision analysis. Control Decis. 23, 361–371 (2008).
[9]
Pan, L. & Deng, Y. A new belief entropy to measure uncertainty of basic probability assignments based on belief function and plausibility function. Entropy 20(11), 842. https://doi.org/10.3390/e20110842 (2018). 10.3390/e20110842
[10]
Popovic, M. Researchers in an entropy wonderland: A review of the entropy concept. Preprint arXiv:1711.07326, 1–29 (2017). https://doi.org/10.48550/arXiv.1711.07326 10.48550/arxiv.1711.07326
[11]
Kuo, W. & Zuo, M. J. Optimal reliability modeling: Principles and applications (John Wiley and Sons, 2001). https://doi.org/10.1198/tech.2004.s742. 10.1198/tech.2004.s742
[12]
Lai, C. D., Murthy, D. N. P. & Xie, M. Weibull distributions. Wiley Interdiscipl. Rev. Comput. Stat. 3(3), 282–287 (2011). 10.1002/wics.157
[13]
Almalki, S. J. & Yuan, J. A new modified Weibull distribution. Reliabil. Eng. Syst. Safety 111, 164–170 (2013). 10.1016/j.ress.2012.10.018
[14]
Marshall, A. W. & Olkin, I. A new method for adding a parameter to a family of distributions with application to the exponential and Weibull families. Biometrika 84, 641–652 (1997). 10.1093/biomet/84.3.641
[15]
Sarhan, A. M. & Zaindin, M. Modified Weibull distribution. APPS Appl. Sci. 11, 123–136 (2009).
[16]
Mudholkar, G. S. & Srivastava, D. K. Exponentiated Weibull family for analyzing bathtub failure-rate data. IEEE Trans. Reliabil. 42(2), 299–302 (1993). 10.1109/24.229504
[17]
Bidram, H., Alamatsaz, M. H. & Nekoukhou, V. On an extension of the exponentiated Weibull distribution. Commun. Stat. Simulat. Comput. 44(6), 1389–1404 (2015). 10.1080/03610918.2013.819918
[18]
Xu, M., Droguett, E. L., Lins, I. D. & das Chagas Moura, M. On the q-Weibull distribution for reliability applications: An adaptive hybrid artificial bee colony algorithm for parameter estimation. Reliabil. Eng. Syst. Safety 158, 93–105 (2017). 10.1016/j.ress.2016.10.012
[19]
Cordeiro, G. M., Lima, M. D. C. S., Gomes, A. E., da Silva, C. Q. & Ortega, E. M. The gamma extended Weibull distribution. J. Statist. Distribut. Appl. 3, 1–19 (2016).
[20]
Carrasco, J. M., Ortega, E. M. & Cordeiro, G. M. A generalized modified Weibull distribution for lifetime modeling. Comput. Statist. Data Analy. 53(2), 450–462 (2008). 10.1016/j.csda.2008.08.023
[21]
Singla, N., Jain, K. & Sharma, S. K. The beta generalized Weibull distribution: Properties and applications. Reliabil. Eng. Syst. Safety 102, 5–15 (2012). 10.1016/j.ress.2012.02.003
[22]
Sarhan, A. M. & Apaloo, J. Exponentiated modified Weibull extension distribution. Reliabil. Eng. Syst. Safety 112, 137–144 (2013). 10.1016/j.ress.2012.10.013
[23]
Méndez-González, L. C., Rodríguez-Picón, L. A., Valles-Rosales, D. J., Alvarado Iniesta, A. & Carreón, A. E. Q. Reliability analysis using exponentiated Weibull distribution and inverse power law. Quality Reliabil. Eng. Int. 35(4), 1219–1230 (2019). 10.1002/qre.2455
[24]
Shakhatreh, M. K., Lemonte, A. J. & Moreno-Arenas, G. The log-normal modified Weibull distribution and its reliability implications. Reliabil. Eng. Syst. Safety 188, 6–22 (2019). 10.1016/j.ress.2019.03.014
[25]
Phani, K. K. A new modified Weibull distribution function. Commun. Am. Ceramic Soc. 70, 182–184 (1987).
[26]
Silva, G. O., Ortega, E. M. & Cordeiro, G. M. The beta modified Weibull distribution. Lifetime Data Analy. 16, 409–430 (2010). 10.1007/s10985-010-9161-1
[27]
Cordeiro, G. M., Hashimoto, E. M. & Ortega, E. M. The McDonald Weibull model. Statistics 48(2), 256–278 (2014). 10.1080/02331888.2012.748769
[28]
A Mathematical Theory of Communication

C. E. Shannon

Bell System Technical Journal 1948 10.1002/j.1538-7305.1948.tb01338.x
[29]
Isaic-Maniu, A. Some comments on an entropy-like transformation of Soleha and Sewilam. Econ. Comput. Econ. Cybernetics Stud. Res. 42, 5–11. https://doi.org/10.1017/S0269964801151077 (2008). 10.1017/s0269964801151077
[30]
Sindhu, T. N., Shafiq, A., Lone, S. A. & Abushal, T. A. The entropy-transformed Gompertz distribution: Distributional insights and cross-disciplinary utilizations. Kuwait J. Sci. 52(1), 100335 (2025). 10.1016/j.kjs.2024.100335
[31]
Ali, A., Naeem, S., Anam, S. & Ahmed, M. M. Entropy in information theory from many perspectives and various mathematical models. J. Appl. Emerg. Sci. 12(2), 156–165 (2022).
[32]
Pan, L. & Deng, Y. A new belief entropy to measure uncertainty of basic probability assignments based on belief function and plausibility function. Entropy 20(11), 842 (2018). 10.3390/e20110842
[33]
Tsallis, C., Mendes, R. & Plastino, A. R. The role of constraints within generalized nonextensive statistics. Phys. A Statist. Mechan. Appl. 261(3–4), 534–554 (1998). 10.1016/s0378-4371(98)00437-3
[34]
Mathai, A. M. & Haubold, H. J. On generalized distributions and pathways. Phys. Lett. A 372(12), 2109–2113 (2008). 10.1016/j.physleta.2007.10.084
[35]
Havrda, J. & Charvat, F. Quantification method of classification processes, Concept of structural-entropy. Kybernetika 3, 30–35 (1967).
[36]
Arimoto, S. Information-theoretical considerations on estimation problems. Inf. Control 19(3), 181–194 (1971). 10.1016/s0019-9958(71)90065-9
[37]
Sindhu, T. N. et al. Introducing the new arcsine-generator distribution family: An in-depth exploration with an illustrative example of the inverse Weibull distribution for analyzing healthcare industry data. J. Radiat. Res. Appl. Sci. 17(2), 100879 (2024).
[38]
Shafiq, A. et al. A new modified Kies Fréchet distribution: Applications of mortality rate of Covid-19. Res. Phys. 28, 104638 (2021).
[39]
Shafiq, A., Sindhu, T. N. & Alotaibi, N. A novel extended model with versatile shaped failure rate: Statistical inference with COVID-19 applications. Res. Phys. 36, 105398 (2022).
[40]
Jia, J., Yan, Z., Song, H. & Chen, Y. Reliability estimation in multicomponent stress–strength model for generalized inverted exponential distribution. Soft Comput. 27(2), 903–916 (2023). 10.1007/s00500-022-07628-1
[41]
Zhuang, L., Xu, A., Wang, Y. & Tang, Y. Remaining useful life prediction for two-phase degradation model based on reparameterized inverse Gaussian process. Eur. J. Operat. Res. 319(3), 877–890 (2024). 10.1016/j.ejor.2024.06.032
[42]
He, D., Sun, D. & Zhu, Q. Bayesian analysis for the Lomax model using noninformative priors. Statist. Theory Related Fields 7(1), 61–68 (2023). 10.1080/24754269.2022.2133466
[43]
Xu, A., Fang, G., Zhuang, L. & Gu, C. A multivariate student-t process model for dependent tail-weighted degradation data. IISE Trans. https://doi.org/10.1080/24725854.2024.2389538 (2024). 10.1080/24725854.2024.2389538
[44]
Alotaibi, R., Nassar, M. & Elshahhat, A. Estimations of modified lindley parameters using progressive type-II censoring with applications. Axioms 12(2), 171 (2023). 10.3390/axioms12020171
[45]
Alomani, G. & Emam, W. A Bayesian and classical estimation of the new probabilistic Inverted Topp Leone Weibull distribution with application. (2023). 10.21203/rs.3.rs-2753910/v1
[46]
Ross, M. S. Introductory statistics 3rd edn, 365 (Elsevier, 2010).
[47]
Lai, C. D., Xie, M. & Murthy, D. N. P. Modified Weibull model. IEEE Trans. Reliabil. 52, 33–37 (2003). 10.1109/tr.2002.805788
[48]
Sindhu, T. N., Shafiq, A. & Huassian, Z. Generalized exponentiated unit Gompertz distribution for modeling arthritic pain relief times data: classical approach to statistical inference. J. Biopharmaceut. Statist. 34(3), 323–348 (2024). 10.1080/10543406.2023.2210681
[49]
Sindhu, T. N., Shafiq, A., Mazucheli, J., Özel, G. & Alves, B. Some additional facts about the unit-Gompertz distribution. Chilean J. Statist. (ChJS) https://doi.org/10.32372/ChJS.14-02-05 (2023). 10.32372/chjs.14-02-05
[50]
Shafiq, A., Sindhu, T. N., Hussain, Z., Mazucheli, J. & Alves, B. A flexible probability model for proportion data: Unit Gumbel type-II distribution, development, properties, different method of estimations and applications. Austrian J. Statist. 52(2), 116–140 (2023). 10.17713/ajs.v52i2.1407
Metrics
11
Citations
50
References
Details
Published
Dec 30, 2024
Vol/Issue
14(1)
License
View
Cite This Article
Tabassum Naz Sindhu, Anum Shafiq, Showkat Ahmad Lone, et al. (2024). Distributional properties of the entropy transformed Weibull distribution and applications to various scientific fields. Scientific Reports, 14(1). https://doi.org/10.1038/s41598-024-83132-w