Full-text resources of CEJSH and other databases are now available in the new Library of Science.
Visit https://bibliotekanauki.pl

PL EN


2025 | 26 | 1 | 93-115

Article title

Type I heavy-tailed family of generalized Burr III distributions: properties, actuarial measures, regression and applications

Content

Title variants

Languages of publication

Abstracts

EN
This study introduces a new family of distributions (FoD) called type I heavy-tailed odd Burr III-G (TI-HT-OBIII-G) distribution. Several statistical properties of the family are derived along with actuarial risk measures. The maximum likelihood estimation (MLE) approach is adopted in the parameter estimation process. The estimates are evaluated centered on mean square errors and average bias via the Monte Carlo simulation framework. A regression model is formulated and the residual analysis is investigated. Members of the new FoD are applied to heavy-tailed data sets and compared to some well-known competing heavytailed distributions. The practicality, flexibility and importance of the new distribution in modeling is empirically proven using three data sets.

Year

Volume

26

Issue

1

Pages

93-115

Physical description

Dates

published
2025

Contributors

author
  • Botswana International University of Science and Technology, Department of Mathematics and Statistical Sciences, Botswana
  • Manicaland State University of Applied Sciences, Department of Applied Statistics, Zimbabwe
  • Botswana International University of Science and Technology, Department of Mathematics and Statistical Sciences, Botswana
  • Botswana International University of Science and Technology, Department of Mathematics and Statistical Sciences, Botswana

References

  • Afify, A. Z., Gemeay, A. M. and Ibrahim, N. A., (2020). The Heavy-Tailed Exponential Distribution: Risk Measures, Estimation, and Application to Actuarial Data. Mathematics, 8(8), 1276.
  • Alizadeh, M., Cordeiro, G., Nascimento, A. and M. Ortega, E. M. M. (2017). Odd Burr Generalized Family of Distributions with some Applications. Journal of Statistical Computation and Simulation, 87, pp. 367-389.
  • Alyami, S. A., Babu, M. G., Elbatal, I., Alotaibi, N. and Elgarhy, M., (2022). Type II Half Logistical Odd Fréchet Class of Distributions: Statistical Theory and Applications. Symmetry, 14(6), 1222. https://doi.org/10.3390/sym14061222
  • Atiknson, A. C., (1985). Plots, Transformations and Regression: An Introduction to Graphical Methods of Diagnostic Regression Analysis. Clarendon Press Oxford. https://doi.org/10.1007/s40300-013-0007
  • Benkhelifa, L., (2022). Alpha Power Topp-Leone Weibull Distribution: Properties, Characterizations, Regression Modeling and Applications. Journal of Statistics and Management Systems, 25(8), pp. 1-26.
  • Cooray, K., Ananda, M. M., (2008). A Generalization of the Half-normal Distribution with Applications to Lifetime Data. Communications in Statistics Theory and Methods, 37(9), pp. 1323-1337.
  • Cordeiro, G. M., Alizadeh, M. and Ortega, E. M., (2014). The Exponentiated HalfLogistic Family of Distributions: Properties and Applications. Journal of Probability and Statistics. https://doi.org/10.1155/2014/864396
  • Cordeiro, G. M., Ortega, E. M. and Nadarajah, S., (2010). The Kumaraswamy Weibull Distribution with Application to Failure Data. Journal of the Franklin Institute, 347(8), pp.1399-1429.
  • Dey, S., Nassar. M. and Kumar, D., (2019). Alpha Power Transformed Inverse Lindley Distribution. A Distribution with an Upside-down Bathtub shaped Hazard Function. Journal of Computational and Applied Mathematics, 348, pp. 130-145.
  • Descheemaeker, L., Grilli. J. and de Buyl, S., (2021). Heavy-tailed Abundance Distributions from Stochastic Lotka-Volterra models. American Physical Society, 104(38), pp. 034404-034413.
  • Korkmaz, M. C., Yousof, H. M. and Hamedani, G. G., (2018). The Exponential Lindley odd Log-Logistic-G Family: Properties, Characterizations and Applications. Journal of Statistical Theory and Applications, 17(3), pp. 554-571.
  • Nelson, W. B., (2004). Accelerated Testing: Statistical Models, Test Plans, and Data Analysis. John Wiley and sons.
  • Rényi, A., (1960). On Measures of Entropy and Information. Proceedings of the Fourth Berkeley. Symposium on Mathematical Statistics and Probability.
  • Shannon, C. E., (1951). Prediction and Entropy of Printed English. The Bell System Technical Journal.
  • Tahir, M., Cordeiro, G. M. and Zubair, M., (2014). The Weibull-Lomax distribution: Properties and Applications. Hacettepe University Bulletin of Natural Sciences and Engineering Series B: Mathematics and Statistics, 10, pp. 147-465.
  • Teamah, A. E. A., Elbanna, A. A. and Gemeay, A. M., (2021). Heavy-tailed Log-Logistic Distribution: Properties, Risk Measures and Applications. Statistics, Optimization, and Information Computing, 9(4), pp. 910-941.
  • Xu, K., Xie, M., Tang, Ching, L. and Ho, S. L., (2003). Application of Neural Networks in Forecasting Engine Systems Reliability. Applied Soft Computing, 2, pp. 255-268.
  • Yousof, H. M., Afify, A. Z., Alizadeh, M., Butt, N. S., Hamedani, G. and Ali, M. M., (2015). The Transmuted Exponentiated Generalized-G Family of Distributions. Pakistan Journal of Statistics and Operation Research, 11(4), pp. 441-464.
  • Zhao, W., Khosa, S. K., Ahmad, Z., Aslam M., and Afify, A. Z., (2020). Type-I HeavyTailed Family with Applications in Medicine, Engineering, and Insurance. PLoS ONE, 15(8). https://doi.org/10.1371/journal.pone.0237462.

Document Type

Publication order reference

Identifiers

Biblioteka Nauki
59315683

YADDA identifier

bwmeta1.element.ojs-doi-10_59139_stattrans-2025-006
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.