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

Generalized DUS-Bilal Distribution: Properties and Applications

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TAThanasate Akkanphudit

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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Cite This Study

Thanasate Akkanphudit (2025) studied this question.

synapsesocial.com/papers/68bb3d552b87ece8dc955e71https://doi.org/10.60101/past.2025.259700
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Also Consider

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  5. 5Modeling Lifetime Data with a Novel Alpha-Power DUS Lindley Distribution2026