The New Ristić-Balakrishnan-Heavy-Tailed-Type II Topp-Leone-G Family of Distributions with Applications

This study introduces a novel statistical family known as the Ristić-Balakrishnan-Heavy-Tailed-Type II Topp-Leone-G (RB-HT-TIITL-G). The research explores key statistical properties, including hazard rate function, quantile function, moments, stochastic ordering, and order statistics. Furthermore, essential risk measures such as value at risk, tail variance, and tail variance premium are derived. Several parameter estimation methods, including maximum likelihood, least squares, weighted least squares, maximum product spacing, Cramér-von Mises, and Anderson-Darling estimators, are applied. Monte Carlo simulations evaluate the consistency of these estimation techniques. Lastly, the model’s effectiveness is validated using real-world data, demonstrating its suitability for modeling diverse data sets.

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Publication Details

Journal
Journal of Statistical Theory and Applications
Published
2025-03-27
DOI
https://doi.org/10.1007/s44199-025-00113-2
Primary Topic
Statistical Distribution Estimation and Applications
Type
article
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article

The New Ristić-Balakrishnan-Heavy-Tailed-Type II Topp-Leone-G Family of Distributions with Applications

Gayan Warahena-Liyanage, Broderick Olusegun Oluyede, Thatayaone Moakofi
Journal of Statistical Theory and Applications
Statistical Distribution Estimation and Applications
article

The New Ristić-Balakrishnan-Heavy-Tailed-Type II Topp-Leone-G Family of Distributions with Applications

Gayan Warahena-Liyanage, Broderick Olusegun Oluyede, Thatayaone Moakofi
article en

Abstract

This study introduces a novel statistical family known as the Ristić-Balakrishnan-Heavy-Tailed-Type II Topp-Leone-G (RB-HT-TIITL-G). The research explores key statistical properties, including hazard rate function, quantile function, moments, stochastic ordering, and order statistics. Furthermore, essential risk measures such as value at risk, tail variance, and tail variance premium are derived. Several parameter estimation methods, including maximum likelihood, least squares, weighted least squares, maximum product spacing, Cramér-von Mises, and Anderson-Darling estimators, are applied. Monte Carlo simulations evaluate the consistency of these estimation techniques. Lastly, the model’s effectiveness is validated using real-world data, demonstrating its suitability for modeling diverse data sets.

Journal of Statistical Theory and ApplicationsVol. 24(2)
University of Dayton (US), Botswana International University of Science and Technology (BW)
Openalex Percentile: Top 94%
Statistical Distribution Estimation and Applications
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