Sine generalized family of distributions: Properties, estimation, simulations and applications
Recent studies have discussed the application and relevance of trigonometry in probability distributions. In this paper, a new, trigonometric family of distributions derived from the alliance of the families known as Sine-G and Generalized G family, inspiring the name Sine Generalized-G Family is pr...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016824010184 |
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| author | Dorathy O. Oramulu Najwan Alsadat Anoop Kumar Mahmoud Mohamed Bahloul Okechukwu J. Obulezi |
| author_facet | Dorathy O. Oramulu Najwan Alsadat Anoop Kumar Mahmoud Mohamed Bahloul Okechukwu J. Obulezi |
| author_sort | Dorathy O. Oramulu |
| collection | DOAJ |
| description | Recent studies have discussed the application and relevance of trigonometry in probability distributions. In this paper, a new, trigonometric family of distributions derived from the alliance of the families known as Sine-G and Generalized G family, inspiring the name Sine Generalized-G Family is proposed and studied. The characteristics of the distribution are explained through the mode, stochastic ordering, quantile function, expansion of the distributional function, and moment. A sub-model of the family named Sine generalized Inverse Gompertz (SG-IG) distribution was studied, its parameters estimated using non-Bayesian and Bayesian procedures. Rigorous simulation studies involving the utilization of the Metropolis–Hasting (MH) algorithm in the Monte Carlo Markov Chain (MCMC) algorithm was carried out to determine the efficiency of the methods and the usefulness was illustrated using two real datasets. The method of Weighted Least squares (WLS) was found to be more efficient in the estimation of parameters of the SG-IG distribution using the lifetime data compared to its competitors. |
| format | Article |
| id | doaj-art-7893c1e2ecd34c76a65fbb3a7de63b00 |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-7893c1e2ecd34c76a65fbb3a7de63b002024-12-21T04:27:52ZengElsevierAlexandria Engineering Journal1110-01682024-12-01109532552Sine generalized family of distributions: Properties, estimation, simulations and applicationsDorathy O. Oramulu0Najwan Alsadat1Anoop Kumar2Mahmoud Mohamed Bahloul3Okechukwu J. Obulezi4Department of Statistics, Faculty of Physical Sciences, Nnamdi Azikiwe University, P.O. Box 5025, Awka, NigeriaDepartment of Quantitative Analysis, College of Business Administration, King Saud University, P.O. Box 71115, Riyadh 11587, Saudi ArabiaDepartment of Statistics, Faculty of Basic Science, Central University of Haryana, Mahendergarh, 123031, IndiaBusiness Information Systems, Faculty of Commerce and Business Administration, Helwan University, Cairo, EgyptDepartment of Statistics, Faculty of Physical Sciences, Nnamdi Azikiwe University, P.O. Box 5025, Awka, Nigeria; Corresponding author.Recent studies have discussed the application and relevance of trigonometry in probability distributions. In this paper, a new, trigonometric family of distributions derived from the alliance of the families known as Sine-G and Generalized G family, inspiring the name Sine Generalized-G Family is proposed and studied. The characteristics of the distribution are explained through the mode, stochastic ordering, quantile function, expansion of the distributional function, and moment. A sub-model of the family named Sine generalized Inverse Gompertz (SG-IG) distribution was studied, its parameters estimated using non-Bayesian and Bayesian procedures. Rigorous simulation studies involving the utilization of the Metropolis–Hasting (MH) algorithm in the Monte Carlo Markov Chain (MCMC) algorithm was carried out to determine the efficiency of the methods and the usefulness was illustrated using two real datasets. The method of Weighted Least squares (WLS) was found to be more efficient in the estimation of parameters of the SG-IG distribution using the lifetime data compared to its competitors.http://www.sciencedirect.com/science/article/pii/S1110016824010184Sine-G familyGeneralized-G familySine generalized inverted Gompertz distributionMaximum likelihoodMomentsReal-life data |
| spellingShingle | Dorathy O. Oramulu Najwan Alsadat Anoop Kumar Mahmoud Mohamed Bahloul Okechukwu J. Obulezi Sine generalized family of distributions: Properties, estimation, simulations and applications Alexandria Engineering Journal Sine-G family Generalized-G family Sine generalized inverted Gompertz distribution Maximum likelihood Moments Real-life data |
| title | Sine generalized family of distributions: Properties, estimation, simulations and applications |
| title_full | Sine generalized family of distributions: Properties, estimation, simulations and applications |
| title_fullStr | Sine generalized family of distributions: Properties, estimation, simulations and applications |
| title_full_unstemmed | Sine generalized family of distributions: Properties, estimation, simulations and applications |
| title_short | Sine generalized family of distributions: Properties, estimation, simulations and applications |
| title_sort | sine generalized family of distributions properties estimation simulations and applications |
| topic | Sine-G family Generalized-G family Sine generalized inverted Gompertz distribution Maximum likelihood Moments Real-life data |
| url | http://www.sciencedirect.com/science/article/pii/S1110016824010184 |
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