A novel asymmetric form of the power half-logistic distribution with statistical inference and real data analysis

Amal S. Hassan; Najwan Alsadat; Mohammed Elgarhy; Laxmi Prasad Sapkota; Oluwafemi Samson Balogun; Ahmed M. Gemeay; Amal S. Hassan; Najwan Alsadat; Mohammed Elgarhy; Laxmi Prasad Sapkota; Oluwafemi Samson Balogun; Ahmed M. Gemeay

doi:10.3934/era.2025036

Electronic Research Archive

2025, Volume 33, Issue 2: 791-825. doi: 10.3934/era.2025036

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Research article

A novel asymmetric form of the power half-logistic distribution with statistical inference and real data analysis

1.
Faculty of Graduate Studies for Statistical Research, Cairo University, 5 Dr. Ahmed Zewail Street, Giza 12613, Egypt
2.
Department of Quantitative Analysis, College of Business Administration, King Saud University, P.O. Box 71115, Riyadh 11587, Saudi Arabia
3.
Department of Basic Sciences, Higher Institute of Administrative Sciences, Belbeis, AlSharkia, Egypt
4.
Department of Statistics, Tribhuvan University, Tribhuvan Multiple Campus, Palpa, Nepal
5.
Department of Computing, University of Eastern Finland, FI-70211, Finland
6.
Department of Mathematics, Faculty of Science, Tanta University, Tanta 31527, Egypt

Received: 09 July 2024 Revised: 30 December 2024 Accepted: 08 January 2025 Published: 12 February 2025

This study provided a significant contribution to developing an adaptable trigonometric extension of the power half-logistic distribution. To be more specific, we created an innovative two-parameter lifetime model called the sine power half-logistic distribution (SPHLD) by using features from the sine-generated family of distributions. The novel distribution could be more effective in modeling lifetime phenomena when asymmetric data was presented, which was the primary motivating factor. The SPHLD's density function plots showed that the distribution adopted several asymmetric shape configurations. Furthermore, the SPHLD's hazard rate plots displayed both monotonic increases and decreases. The quantile function, moments, incomplete moment, and stress-strength reliability were among the statistical characteristics of the SPHLD that were computed. Statistical inference using sixteen distinct classical estimating techniques was utilized to estimate the SPHLD parameters. A simulation study was done to evaluate the consistency of the different estimates and determine the best estimating approach based on some accuracy measures. Analyses of real data revealed that the SPHLD performed better than a number of alternative distributions.
Citation: Amal S. Hassan, Najwan Alsadat, Mohammed Elgarhy, Laxmi Prasad Sapkota, Oluwafemi Samson Balogun, Ahmed M. Gemeay. A novel asymmetric form of the power half-logistic distribution with statistical inference and real data analysis[J]. Electronic Research Archive, 2025, 33(2): 791-825. doi: 10.3934/era.2025036

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Abstract

This study provided a significant contribution to developing an adaptable trigonometric extension of the power half-logistic distribution. To be more specific, we created an innovative two-parameter lifetime model called the sine power half-logistic distribution (SPHLD) by using features from the sine-generated family of distributions. The novel distribution could be more effective in modeling lifetime phenomena when asymmetric data was presented, which was the primary motivating factor. The SPHLD's density function plots showed that the distribution adopted several asymmetric shape configurations. Furthermore, the SPHLD's hazard rate plots displayed both monotonic increases and decreases. The quantile function, moments, incomplete moment, and stress-strength reliability were among the statistical characteristics of the SPHLD that were computed. Statistical inference using sixteen distinct classical estimating techniques was utilized to estimate the SPHLD parameters. A simulation study was done to evaluate the consistency of the different estimates and determine the best estimating approach based on some accuracy measures. Analyses of real data revealed that the SPHLD performed better than a number of alternative distributions.

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