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 Shahrood University of Technology

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.



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

Main Subjects

• Statistical Modelling