Developing flexible classes of distributions to account for both skewness and bimodality

TL;DR

This paper introduces two new methods for creating flexible, skewed, and bimodal distributions that extend classical models like normal and Laplace, with applications demonstrated on real data.

Contribution

The paper proposes novel approaches for constructing skewed and bimodal distributions, generalizing classical symmetric models with practical estimation and application methods.

Findings

New distributions effectively model skewness and bimodality.

Maximum likelihood estimation is suitable for parameter inference.

Applications demonstrate real-world relevance of the distributions.

Abstract

We develop two novel approaches for constructing skewed and bimodal flexible 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.