A novel technique for generating families of continuous distributions

  • Boikanyo Makubate Botswana International University of Science and Technology
  • Regent Retrospect Musekwa Botswana International University of Science and Technology
DOI: https://doi.org/10.19139/soic-2310-5070-2068
Keywords: New flexible-G, Generalized flexible-G, Family of distributions, Continuous distributions, Maximum likelihood estimation

Abstract

In this paper, we present the generalized flexible-G family for creating several continuous distributions. Our new technique features are that it adds only two extra shape parameters to any chosen continuous distribution and is not derived from any parent distribution that currently exists. Several special cases of this family are provided. The generalized flexible-G family offers significant improvements in flexibility, fit, and applicability across a wide range of fields. The family's model parameters are estimated using the maximum likelihood estimation method. A simulation study is conducted to assess the consistency of the maximum likelihood estimates. The generalized flexible log-logistic, a specific case of our novel family, is applied to both patient's analgesia and reliability data in order to illustrate the significance of the family. The generalized flexible log-logistic outperforms several competitive models provided in this paper. Furthermore, the generalized flexible log-logistic performs better than traditional distributions such as the BurrXII, Gumbel, and Weibull models.
Published
2024-07-29
How to Cite
Makubate, B., & Musekwa, R. R. (2024). A novel technique for generating families of continuous distributions. Statistics, Optimization & Information Computing, 12(5), 1231-1248. https://doi.org/10.19139/soic-2310-5070-2068
Issue
Vol 12 No 5 (2024)
Section
Research Articles
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