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April 22, 2026Mathematics0 citationsOpen Access

The q-Deformed Lindley Distribution: Properties, Statistical Inference, and Applications

MEMahmoud M. El-AwadyHAHanan Haj Haj AhmadYRYazan Rabaiah

Key Points

  • The aim is to develop a q-deformed version of the Lindley distribution to better model right-skewed data and assess its properties and applications.
  • Introduced a q-deformed extension using the q-exponential function from Tsallis statistics.
  • Derived key properties such as quantile function, moments, and Tsallis entropy.
  • Conducted Monte Carlo simulations for parameter estimation and method performance evaluation.
  • The q-deformed Lindley distribution provides enhanced control over tail behavior compared to classical models.
  • It was demonstrated to model heavy-tailed and highly skewed data more effectively.
  • Applications to real-world datasets indicated superior performance relative to traditional distributions.

Abstract

This paper introduces a q-deformed extension of the Lindley distribution. This extension is obtained by replacing the classical exponential with the q-exponential function from Tsallis non-extensive statistical techniques. This transformation offers more control over the tail behavior of the distribution, providing a transition between exponential and power-law decay patterns. Such flexibility is particularly useful when modeling right-skewed data with excess kurtosis, where classical models may not adequately describe heavy-tailed and highly skewed data. We derive several key properties, including the quantile function, expressed by the Lambert–Tsallis function Wq, the raw and incomplete moments, the probability-weighted moments, and the Tsallis entropy. The distribution of order statistics is also investigated. For parameter estimation, we employ several frequentist methods and conduct extensive Monte Carlo simulation studies to assess and compare their performance. Finally, applications to real-world datasets show that the q-deformed Lindley model is practically superior and more flexible than the classical Lindley distribution and other well-known models.

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Cite This Study

El-Awady et al. (2026) studied this question.

synapsesocial.com/papers/69e866896e0dea528ddead94https://doi.org/10.3390/math14081364
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