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September 5, 2025Progress in Applied Science and Technology1 citationsOpen Access

Generalized DUS-Bilal Distribution: Properties and Applications

Key Points

The generalized dus transformation introduces a flexible version of the bilal distribution.
Three parameter estimation methods, including maximum likelihood, show distinct applicability on real datasets.
Anderson-Darling and Cramer-Von Mises techniques complement the maximum likelihood approach in parameter estimation.
Studying this distribution's properties enhances its application in diverse statistical modeling scenarios.

Abstract

In this article, we proposed a flexible version of the Bilal distribution using the generalized DUS transformation. Its properties are studied. Three methods of parameter estimation, including the maximum likelihood, Anderson-Daring, and Cramer-Von Mises techniques, are used to estimate unknown parameters. Real datasets are used to demonstrate the applicability of the proposed distribution using the three methods of parameter estimation.

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