The Weibull Topp-Leone Generated Family of Distributions: Statistical Properties and Applications

Authors

1 Department of Statistics, Persian Gulf University, Bushehr, Iran

2 Department of Statistics, Mathematics and Insurance, Benha University, Benha, Egypt

3 Department of Mathematics, Statistics and Computer Science , Marquette University, USA

10.29252/jirss.19.1.121

Abstract

Statistical distributions are very useful in describing and predicting real world phenomena. Consequently, the choice of the most suitable statistical distribution for modeling given data is very important. In this paper, we propose a new class of lifetime distributions called the Weibull Topp-Leone Generated (WTLG) family. The proposed family is constructed via compounding the Weibull and the Topp-Leone distributions. It can provide better fits and is very flexible in comparison with the various known lifetime distributions. Several general statistical properties of the WTLG family are studied in details including density and hazard shapes, limit behavior, mixture representation, skewness and kurtosis, moments, moment generating function, incomplete moment. Different methods have been used to estimate its parameters. The performances of the estimators are numerically investigated. We have discussed inference on the new family based on the likelihood ratio statistics for testing some lifetime distributions. We assess the performance of the maximum likelihood estimators in terms of the biases and mean squared errors by means of a simulation study. The importance and flexibility of the new family are illustrated by means of two applications to real data sets.

Keywords

  1. Afify, A. Z., Yousof, H. M. and Nadarajah, S. (2017), The beta transmuted-H family of distributions: properties and applications. Stasistics and its Inference, 10, 505-520. [DOI:10.4310/SII.2017.v10.n3.a13]
  2. Alizadeh, M., Ghosh, I., Yousof, H. M., Rasekhi, M. and Hamedani G. G. (2017), The generalized odd generalized exponential family of distributions: properties, characterizations and applications. Journal of Data Science, 16, 443-446.
  3. Alizadeh, M., Korkmaz, M. C., Almamy, J. A. and Ahmed, A. A. E. (2018), Another odd log-logistic logarithmic class of continuous distributions. Journal of Statisticians: Statistics and Actuarial Sciences, 11(2), 55-72.
  4. Anderson, T. W. and Darling, D. A. (1952), Asymptotic theory of certain" goodness of fit" criteria based on stochastic processes. The annals of mathematical statistics, 193-212. [DOI:10.1214/aoms/1177729437]
  5. Alzaatreh, A., Lee, C. and Famoye, F. (2013), A new method for generating families of continuous distributions. Metron, 71, 63-79. [DOI:10.1007/s40300-013-0007-y]
  6. Alzaatreh, A., Famoye, F. and Lee, C. (2014), The gamma-normal distribution: Properties and applications. Computational Statistics and Data Analysis, 69, 67-80. [DOI:10.1016/j.csda.2013.07.035]
  7. Brito, E., Cordeiro, G. M., Yousof, H. M., Alizadeh, M. and Silva , G. O. (2017), Topp-Leone odd log-logistic family of distribution. Journal of Statistical Computation and Simulation, 87(15), 3040-3058. [DOI:10.1080/00949655.2017.1351972]
  8. Bourguignon, Silva M., R. B. and Cordeiro, G. M. (2014), The Weibull-G Family of Probability Distributions. Journal of Data Science, 12, 53-68.
  9. Choi, K. and Bulgren,W. (1968), Anestimation procedure for mix- tures of distributions. Journal of the Royal Statistical Society. Series B (Methodological), 444-460. [DOI:10.1111/j.2517-6161.1968.tb00743.x]
  10. Cooray, K. (2006), Generalization of the Weibull distribution: the odd Weibull family. Statistical Modelling, 6, 265-277. [DOI:10.1191/1471082X06st116oa]
  11. Cooray, K. and Ananda, M. M. (2008), A generalization of the half-normal distribution with applications to lifetime data. Communications in Statistics-Theory and Methods, 37(9), 1323-1337. [DOI:10.1080/03610920701826088]
  12. Cordeiro, G. M., Ortega, E. M. M. and Nadarajah, S. (2010), The Kumaraswamy Weibull distribution with application to failure data. Journal of the Franklin Institute, 347, 1399-1429. [DOI:10.1016/j.jfranklin.2010.06.010]
  13. Cordeiro, G. M., Ortega, E. M. and da Cunha, D. C. C. (2013), The exponentiated generalized class of distributions. Journal of Data Science, 11, 1-27.
  14. Dey. S., Mazucheli, J. and Nadarajah.S. (2017), Kumaraswamy distribution: different methods of estimation. Computational and Applied Mathematics, 1-18.
  15. Eugene, N., Lee, C., and Famoye, F. (2002), Beta-normal distribution and its applications. Communications in Statistics - Theory and Methods, 31, 497-512. [DOI:10.1081/STA-120003130]
  16. Famoye, F., Lee, C. and Olumolade, O. (2005), The beta-Weibull distribution. Journal of Statistical Theory and Applications, 4(2), 121-136.
  17. Fonseca, M. B. and Franca, M. G. C. (2007), A influoencia da fertilidade do solo e caracterizaca da fixacao biologica de N2 para o crescimento de Dimorphandra wilsonii rizz. Master thesis, Universidade Federal de Minas Gerais.
  18. Glänzell, W. (1990), Some consequences of a characterization theorem based on truncated moments. Statistics, 21(4), 613-618. [DOI:10.1080/02331889008802273]
  19. Gupta, R. D. and Kundu, D. (2001), Exponentiated exponential family: an alternative to gamma and Weibull. Biometrical Journal, 43, 117-130. https://doi.org/10.1002/1521-4036(200102)43:1<117::AID-BIMJ117>3.0.CO;2-R [DOI:10.1002/1521-4036(200102)43:13.0.CO;2-R]
  20. Gupta, R. C., Gupta, P. L. and Gupta, R. D. (1998), Modeling failure time data by Lehmann alternatives.Communications in Statistics - Theory and Methods, 27, 887-904. [DOI:10.1080/03610929808832134]
  21. Hamedani, G. G. (2013), On certain generalized gamma convolution distributions II (No. 484). Technical Report.
  22. Hamedani, G. G., Altun, E., Korkmaz, M. C., Yousof, H. M. and Butt, N. S. (2018), A new extended G family of continuous distributions with mathematical properties, characterizations and regression modeling. Pakistan Journal of Statistics and Operation [DOI:10.18187/pjsor.v14i3.2484]
  23. Research, 14(3), 737-758.
  24. Hashimoto, E. M, Ortega, E. M. M., Cordeiro, G. M. and Pascoa, M. A. R. (2015), The McDonald Extended Weibull Distribution. Journal of Statistical Theory and Practice, 9(3), 608-632. [DOI:10.1080/15598608.2014.977980]
  25. Korkmaz, M. C. and Genc, A. I. (2017), A new generalized two-sided class of distributions with an emphasis on two- sided generalized normal distribution. Communications in Statistics-Simulation and Computation, 46(2), 1441-1460. [DOI:10.1080/03610918.2015.1005233]
  26. Korkmaz, M. C., Yousof, H. M. and Hamedani, G. G. (2018a), The Exponential Lindley Odd Log-Logistic-G Family: Properties, Characterizations and Applications. Journal of Statistical Theory and Applications, 17(3), 554-571. [DOI:10.2991/jsta.2018.17.3.10]
  27. Korkmaz, M. C., Yousof H. M., Hamedani, G. G. and Ali M. M. (2018b), The Marshall-Olkin Generalized G Poisson Family Of Distributions. Pakistan Journal of Statistics, 34(3), 251-267.
  28. Korkmaz, M. C., Alizadeh, M., Yousof, H. M. and Butt, N. S. (2018c), The generalized oddWeibull generated family of distributions: statistical properties and applications. Pakistan Journal of Statistics and Operation Research, 14(3), 541-556. [DOI:10.18187/pjsor.v14i3.2598]
  29. Korkmaz, M. C. (2019a), A new family of the continuous distributions: the extended Weibull-G family. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics 68(1), 248-270. [DOI:10.31801/cfsuasmas.451602]
  30. Korkmaz, M. C., Cordeiro, G. M., Yousof, H. M., Pescim, R. R., Afify, A. Z., and Nadarajah, S. (2019b), The Weibull Marshall-Olkin family: Regression model and application to censored data. Communications in Statistics-Theory and Methods Accepted, DOI:10.1080/03610926.2018.1490430. [DOI:10.1080/03610926.2018.1490430]
  31. Lindley, D. V. (1958), Fiducial distributions and Bayes' theorem. Journal of the Royal Statistical Society, Series B, 20, 102-107. [DOI:10.1111/j.2517-6161.1958.tb00278.x]
  32. Marshall, A.W. and Olkin, I. (1997), A new methods for adding a parameter to a family of distributions with application to the Exponential andWeibull families. Biometrika, 84, 641-652. [DOI:10.1093/biomet/84.3.641]
  33. Mudholkar, G. S. and Srivastava, D. K. (1993), Exponentiated Weibull family for analysing bathtub failure rate data. IEEE Transactions on Reliability, 42, 299-302. [DOI:10.1109/24.229504]
  34. Nadarajah, S., Cordeiro, G. M., and Ortega, E. M. M., (2014), The Zografos-Balakrishnan-G Family of Distributions: Mathematical Properties and Applications. Communications in Statistics - Theory and Methods, 44, 186-215. [DOI:10.1080/03610926.2012.740127]
  35. Nofal, Z. M., Afify, A. Z., Yousof, H. M. and Cordeiro, G. M. (2017), The generalized transmuted-G family of distributions. Communications in Statistics - Theory and Methods, 46, 4119-4136. [DOI:10.1080/03610926.2015.1078478]
  36. Pogany, T. K., Saboor, A. and Provost, S., (2015), The Marshall Olkin Exponential Weibull Distribution. Hacettepe Journal of Mathematics and Statistics, 44(6), 1579-1594.
  37. Silva, R. B., Bourguignon, M., Dias, C. R. B. and Cordeiro, G. M. (2013), The compound class of extendedWeibull power series distributions. Computational Statistics andData Analysis, 58, 352-367. [DOI:10.1016/j.csda.2012.09.009]
  38. Swain, J. J., Venkatraman, S., andWilson, J. R., (1988), Least- squares estimation of distribution functions in johnson's translation system. Journal of Statistical Computation and Simulation, 29, 271- 297. [DOI:10.1080/00949658808811068]
  39. Yousof, H. M., Afify, A. Z., Alizadeh, M., Butt, N. S., Hamedani, G. G. and Ali, M. M. (2015), The transmuted exponentiated generalized-G family of distributions. Pakistan Journal of Statistics and Operation Research, 11, 441-464. [DOI:10.18187/pjsor.v11i4.1164]
  40. Yousof, H. M., Afify, A. Z., Hamedani, G. G. and Aryal, G. (2016), the Burr X generator of distributions for lifetime data. Journal of Statistical Theory and Applications, 16, 288-305. [DOI:10.2991/jsta.2017.16.3.2]
  41. Yousof, H. M., Majumder, M., Jahanshahi, S. M. A., Ali, M. M. and Hamedani, G. G. (2018), A new Weibull class of distributions: theory, characterizations, and applications. Journal of Statistical Research of Iran, 23, 13-31. [DOI:10.29252/jsri.15.1.45]
Volume 19, Issue 1
June 2020
Pages 121-161
  • Receive Date: 23 July 2022
  • Revise Date: 20 May 2024
  • Accept Date: 23 July 2022