Developing Flexible Classes of Distributions to Account for both Skewness and Bimodality

Document Type : Original Article

Authors
Jamil Ownuk 1
Ahmad Nezakati 2
Hossein Baghishani 1
1 Department of Statistics, Faculty of Mathematical Sciences, Shahrood University of Technology, Shahrood, Iran
2 Department of Statistics, Faculty of Mathematical Sciences, Shahrood University of Technology, Shahrood, Iran.
10.22034/jirss.2023.1999147.1012
Abstract
We develop two novel approaches for constructing flexible skewed and bimodal distributions that can effectively generalize classical symmetric distributions. We illustrate the application of introduced techniques by extending normal, student-t, and Laplace distributions. We also study the properties of the newly constructed distributions. The method of maximum likelihood is proposed for estimating the model parameters. Furthermore, the application of new distributions is represented using real-life data.
Keywords
Bimodal Distributions
ML Estimation
Skewed Distributions
Symmetric Density Functions
Subjects
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Journal of the Iranian Statistical Society
Volume 22, Issue 2
December 2023
Pages 1-33

  • Receive Date 28 March 2023
  • Revise Date 30 September 2023
  • Accept Date 15 October 2023