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Generalization method of generating the continuous nested distributions

  • Mian Muhammad Farooq , Muhammad Mohsin , Muhammad Farman , Ali Akgül EMAIL logo und Muhammad Umer Saleem
Veröffentlicht/Copyright: 20. Mai 2022
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Abstract

In many life time scenarios, life of one component or system nested in other components or systems. To model these complex structures some so called nested models are required rather than conventional models. This paper introduces the generalization of the method of generating continuous distribution proposed by N. Eugene, C. Lee, and F. Famoye, “Beta-normal distribution and its applications,” Commun. Stat. Theor. Methods, vol. 31, no. 4, pp. 497–512, 2002 and A. Alzaatreh, C. Lee, and F. Famoye, “A new method for generating families of continuous distributions,” Metron, vol. 71, no. 1, pp. 63–79, 2013 which nest one model in other to cope with complex systems. Some important characteristics of the proposed family of generalized distribution have been studied. The famous Beta, Kumaraswami and Gamma generated distributions are special cases of our suggested procedure. Some new distributions have also been developed by using the suggested methodology and their important properties have been discussed as well. A variety of real life data sets are used to demonstrate the efficacy of new suggested distributions and illation is made with baseline models.


Corresponding author: Ali Akgül, Department of Mathematics, Arts and Science Faculty, Siirt University, Siirt 56100, Türkiye, E-mail:

  1. Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: None declared.

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

References

[1] K. Pearson, “Contributions to the mathematical theory of evolution. ii. skew variation in homogeneous material,” Phil. Trans. Roy. Soc. Lond.: Math. Phys. Eng. Sci., vol. 186, pp. 343–414, 1895. https://doi.org/10.1098/rsta.1895.0010.Suche in Google Scholar

[2] I. W. Burr, “Cumulative frequency functions,” Ann. Math. Stat., vol. 13, no. 2, pp. 215–232, 1942. https://doi.org/10.1214/aoms/1177731607.Suche in Google Scholar

[3] N. L. Johnson, “Systems of frequency curves generated by methods of translation,” Biometrika, vol. 36, nos. 1–2, pp. 149–176, 1949. https://doi.org/10.1093/biomet/36.1-2.149.Suche in Google Scholar

[4] J. Tukey, The Practical Relationship Between the Common Transformations of Percentages of Counts and Amounts (Tech. Rep. No. 36), Princeton, NJ, Statistical Techniques Research Group, Princeton University, 1960.Suche in Google Scholar

[5] J. S. Ramberg and B. W. Schmeiser, “An approximate method for generating symmetric random variables,” Commun. ACM, vol. 15, no. 11, pp. 987–990, 1972. https://doi.org/10.1145/355606.361888.Suche in Google Scholar

[6] J. S. Ramberg and B. W. Schmeiser, “An approximate method for generating asymmetric random variables,” Commun. ACM, vol. 17, no. 2, pp. 78–82, 1974. https://doi.org/10.1145/360827.360840.Suche in Google Scholar

[7] J. S. Ramberg, E. J. Dudewicz, P. R. Tadikamalla, and E. F. Mykytka, “A probability distribution and its uses in fitting data,” Technometrics, vol. 21, no. 2, pp. 201–214, 1979. https://doi.org/10.1080/00401706.1979.10489750.Suche in Google Scholar

[8] M. Freimer, G. Kollia, G. S. Mudholkar, and C. T. Lin, “A study of the generalized tukey lambda family,” Commun. Stat. Theor. Methods, vol. 17, no. 10, pp. 3547–3567, 1988. https://doi.org/10.1080/03610928808829820.Suche in Google Scholar

[9] Z. A. Karian and E. J. Dudewicz, Fitting Statistical Distributions: The Generalized Lambda Distribution and Generalized Bootstrap Methods, CRC Press, 2000.10.1201/9781420038040Suche in Google Scholar

[10] A. Azzalini, “A class of distributions which includes the normal ones,” Scand. J. Stat., vol. 12, no. 2, pp. 171–178, 1985.Suche in Google Scholar

[11] R. D. Gupta and D. Kundu, “Exponentiated exponential family: an alternative to gamma and Weibull distributions,” Biom. J., vol. 43, no. 1, pp. 117–130, 2001. https://doi.org/10.1002/1521-4036(200102)43:1<117::aid-bimj117>3.0.co;2-r.10.1002/1521-4036(200102)43:1<117::AID-BIMJ117>3.0.CO;2-RSuche in Google Scholar

[12] N. Eugene, C. Lee, and F. Famoye, “Beta-normal distribution and its applications,” Commun. Stat. Theor. Methods, vol. 31, no. 4, pp. 497–512, 2002. https://doi.org/10.1081/sta-120003130.Suche in Google Scholar

[13] M. Jones, “Families of distributions arising from distributions of order statistics,” Test, vol. 13, no. 1, pp. 1–43, 2004. https://doi.org/10.1007/bf02602999.Suche in Google Scholar

[14] S. Nadarajah and S. Kotz, “The beta Gumbel distribution,” Math. Probl Eng., vol. 2004, no. 4, pp. 323–332, 2004. https://doi.org/10.1155/s1024123x04403068.Suche in Google Scholar

[15] S. Nadarajah and S. Kotz, “The beta exponential distribution,” Reliab. Eng. Syst. Saf., vol. 91, no. 6, pp. 689–697, 2006. https://doi.org/10.1016/j.ress.2005.05.008.Suche in Google Scholar

[16] F. Famoye, C. Lee, and O. Olumolade, “The beta-Weibull distribution,” J. Stat. Theory App., vol. 4, no. 2, pp. 121–136, 2005.Suche in Google Scholar

[17] L. Kong, C. Lee, and J. Sepanski, “On the properties of beta-gamma distribution,” J. Mod. Appl. Stat. Methods, vol. 6, no. 1, p. 18, 2007. https://doi.org/10.22237/jmasm/1177993020.Suche in Google Scholar

[18] A. Akinsete, F. Famoye, and C. Lee, “The beta-pareto distribution,” Statistics, vol. 42, no. 6, pp. 547–563, 2008. https://doi.org/10.1080/02331880801983876.Suche in Google Scholar

[19] M. Jones, “Kumaraswamy’s distribution: a beta-type distribution with some tractability advantages,” Stat. Methodol., vol. 6, no. 1, pp. 70–81, 2009. https://doi.org/10.1016/j.stamet.2008.04.001.Suche in Google Scholar

[20] G. M. Cordeiro and M. de Castro, “A new family of generalized distributions,” J. Stat. Comput. Simulat., vol. 81, no. 7, pp. 883–898, 2011. https://doi.org/10.1080/00949650903530745.Suche in Google Scholar

[21] P. Kumaraswamy, “A generalized probability density function for double-bounded random processes,” J. Hydrol., vol. 46, no. 1, pp. 79–88, 1980. https://doi.org/10.1016/0022-1694(80)90036-0.Suche in Google Scholar

[22] G. M. Cordeiro, E. M. Ortega, and S. Nadarajah, “The Kumaraswamy Weibull distribution with application to failure data,” J. Franklin Inst., vol. 347, no. 8, pp. 1399–1429, 2010. https://doi.org/10.1016/j.jfranklin.2010.06.010.Suche in Google Scholar

[23] M. A. de Pascoa, E. M. Ortega, and G. M. Cordeiro, “The Kumaraswamy generalized gamma distribution with application in survival analysis,” Stat. Methodol., vol. 8, no. 5, pp. 411–433, 2011. https://doi.org/10.1016/j.stamet.2011.04.001.Suche in Google Scholar

[24] G. M. Cordeiro, R. R. Pescim, and E. M. Ortega, “The Kumaraswamy generalized half-normal distribution for skewed positive data,” J. Data Sci., vol. 10, no. 2, pp. 195–224, 2012.10.6339/JDS.201204_10(2).0003Suche in Google Scholar

[25] J. T. A. S. Ferreira and M. F. J. Steel, “A constructive representation of univariate skewed distributions,” J. Am. Stat. Assoc., vol. 101, no. 474, pp. 823–829, 2006. https://doi.org/10.1198/016214505000001212.Suche in Google Scholar

[26] A. Alzaatreh, C. Lee, and F. Famoye, “A new method for generating families of continuous distributions,” Metron, vol. 71, no. 1, pp. 63–79, 2013. https://doi.org/10.1007/s40300-013-0007-y.Suche in Google Scholar

[27] G. M. Cordeiro, E. M. Ortega, and D. C. da Cunha, “The exponentiated generalized class of distributions,” J. Data Sci., vol. 11, no. 1, pp. 1–27, 2013.10.6339/JDS.201301_11(1).0001Suche in Google Scholar

[28] M. Mahdy and B. Ahmed, “Skew-generalized inverse Weibull distribution and its properties,” Pak. J. Stat. Oper. Res., vol. 32, no. 5, pp. 329–348, 2016.Suche in Google Scholar

[29] M. Rasekhi, G. Hamedani, and R. Chinipardaz, “A flexible extension of skew generalized normal distribution,” Metron, vol. 75, no. 1, pp. 87–107, 2017. https://doi.org/10.1007/s40300-017-0106-2.Suche in Google Scholar

[30] I. Ghosh and A. Alzaatreh, “A new class of generalized logistic distribution,” Commun. Stat. Theor. Methods, vol. 47, no. 9, pp. 2043–2055, 2018. https://doi.org/10.1080/03610926.2013.835420.Suche in Google Scholar

[31] G. M. Cordeiro, A. Z. Afify, E. M. Ortega, A. K. Suzuki, and M. E. Mead, “The odd lomax generator of distributions: properties, estimation and applications,” J. Comput. Appl. Math., vol. 347, pp. 222–237, 2019. https://doi.org/10.1016/j.cam.2018.08.008.Suche in Google Scholar

[32] E. Mahmoudi, H. Jafari, and R. Meshkat, “Alpha-skew generalized normal distribution and its applications,” Appl. Appl. Math., vol. 14, no. 2, pp. 784–804, 2019.10.29252/jsri.14.2.219Suche in Google Scholar

[33] M. A. Aljarrah, F. Famoye, and C. Lee, “A new generalized normal distribution: properties and applications,” Commun. Stat. Theor. Methods, vol. 48, no. 18, pp. 4474–4491, 2019. https://doi.org/10.1080/03610926.2018.1483509.Suche in Google Scholar

[34] M. A. Aljarrah, F. Famoye, and C. Lee, “Generalized logistic distribution and its regression model,” J. Stat. Distrib. Appl., vol. 7, no. 1, pp. 1–21, 2020. https://doi.org/10.1186/s40488-020-00107-8.Suche in Google Scholar

[35] J. H. Guardiola, “The spherical-Dirichlet distribution,” J. Stat. Distrib. Appl., vol. 7, no. 1, pp. 1–14, 2020. https://doi.org/10.1186/s40488-020-00106-9.Suche in Google Scholar

[36] M. Ijaz, W. K. Mashwani, A. Göktaş, and Y. A. Unvan, “A novel alpha power transformed exponential distribution with real-life applications,” J. Appl. Stat., vol. 48, no. 11, pp. 1–16, 2021.10.1080/02664763.2020.1870673Suche in Google Scholar PubMed PubMed Central

[37] R Core Team, R: A Language and Environment for Statistical Computing [Computer Software Manual], Vienna, Austria, R Team, 2016. Available at: http://www.R-project.org/.Suche in Google Scholar

[38] T. Ryan, B. Joiner, and B. Ryan, Minitab Student Handbook, Hoboken, New Jersey, Canada, John Wiley & Sons, Inc., 1976. Available at: https://books.google.com.pk/books?id=hRH0piMuseMC.Suche in Google Scholar

[39] D. Murthy, M. Xie, and R. Jiang, Weibull Models, Wiley, New York, 2004. Available at: https://books.google.com.pk/books?id=1c5B6w9RZHYC.Suche in Google Scholar

[40] P. Manavalan and W. C. JohnsonJr, “Variable selection method improves the prediction of protein secondary structure from circular dichroism spectra,” Anal. Biochem., vol. 167, no. 1, pp. 76–85, 1987. https://doi.org/10.1016/0003-2697(87)90135-7.Suche in Google Scholar PubMed

Received: 2021-06-09
Revised: 2021-10-16
Accepted: 2022-04-18
Published Online: 2022-05-20

© 2022 Walter de Gruyter GmbH, Berlin/Boston

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