Bayesian inference using MCMC algorithm of sine truncated Lomax distribution with application
This study makes a significant contribution to the creation of a versatile trigonometric extension of the well-known truncated Lomax distribution. Specifically, we construct a novel one-parameter distribution known as the sine truncated Lomax (STLo) distribution using characteristics from the sine g...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
AIP Publishing LLC
2023-09-01
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| Series: | AIP Advances |
| Online Access: | http://dx.doi.org/10.1063/5.0172421 |
| _version_ | 1797663164949594112 |
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| author | Mohammed. Elgarhy Najwan Alsadat Amal S. Hassan Christophe Chesneau |
| author_facet | Mohammed. Elgarhy Najwan Alsadat Amal S. Hassan Christophe Chesneau |
| author_sort | Mohammed. Elgarhy |
| collection | DOAJ |
| description | This study makes a significant contribution to the creation of a versatile trigonometric extension of the well-known truncated Lomax distribution. Specifically, we construct a novel one-parameter distribution known as the sine truncated Lomax (STLo) distribution using characteristics from the sine generalized family of distributions. Quantiles, moments, stress–strength reliability, some information measures, residual moments, and reversed residual moments are a few of the crucial elements and characteristics we explored in our research. The flexibility of the STLo distribution in terms of the forms of the hazard rate and probability density functions illustrates how effectively it is able to match many types of data. Maximum likelihood and Bayesian estimation techniques are used to estimate the model parameter. The squared error loss function is employed in the Bayesian approach. To evaluate how various estimates behave, a Monte Carlo simulation study is carried out with the aid of a useful algorithm. Additionally, the STLo distribution has a good fit, making it a viable option when compared to certain other competing models using specific criteria to describe the given dataset. |
| first_indexed | 2024-03-11T19:09:35Z |
| format | Article |
| id | doaj.art-dab5cf259ada475f9c8343a9dce3e9d1 |
| institution | Directory Open Access Journal |
| issn | 2158-3226 |
| language | English |
| last_indexed | 2024-03-11T19:09:35Z |
| publishDate | 2023-09-01 |
| publisher | AIP Publishing LLC |
| record_format | Article |
| series | AIP Advances |
| spelling | doaj.art-dab5cf259ada475f9c8343a9dce3e9d12023-10-09T20:09:21ZengAIP Publishing LLCAIP Advances2158-32262023-09-01139095120095120-1310.1063/5.0172421Bayesian inference using MCMC algorithm of sine truncated Lomax distribution with applicationMohammed. Elgarhy0Najwan Alsadat1Amal S. Hassan2Christophe Chesneau3Mathematics and Computer Science Department, Faculty of Science, Beni-Suef University, Beni-Suef 62521, EgyptDepartment of Quantitative Analysis, College of Business Administration, King Saud University, P.O. Box 71115, Riyadh 11587, Saudi ArabiaFaculty of Graduate Studies for Statistical Research, Cairo University, 5 Dr. Ahmed Zewail Street, Giza 12613, EgyptDepartment of Mathematics, Université de Caen Normandie, Campus II, Science 3, 14032 Caen, FranceThis study makes a significant contribution to the creation of a versatile trigonometric extension of the well-known truncated Lomax distribution. Specifically, we construct a novel one-parameter distribution known as the sine truncated Lomax (STLo) distribution using characteristics from the sine generalized family of distributions. Quantiles, moments, stress–strength reliability, some information measures, residual moments, and reversed residual moments are a few of the crucial elements and characteristics we explored in our research. The flexibility of the STLo distribution in terms of the forms of the hazard rate and probability density functions illustrates how effectively it is able to match many types of data. Maximum likelihood and Bayesian estimation techniques are used to estimate the model parameter. The squared error loss function is employed in the Bayesian approach. To evaluate how various estimates behave, a Monte Carlo simulation study is carried out with the aid of a useful algorithm. Additionally, the STLo distribution has a good fit, making it a viable option when compared to certain other competing models using specific criteria to describe the given dataset.http://dx.doi.org/10.1063/5.0172421 |
| spellingShingle | Mohammed. Elgarhy Najwan Alsadat Amal S. Hassan Christophe Chesneau Bayesian inference using MCMC algorithm of sine truncated Lomax distribution with application AIP Advances |
| title | Bayesian inference using MCMC algorithm of sine truncated Lomax distribution with application |
| title_full | Bayesian inference using MCMC algorithm of sine truncated Lomax distribution with application |
| title_fullStr | Bayesian inference using MCMC algorithm of sine truncated Lomax distribution with application |
| title_full_unstemmed | Bayesian inference using MCMC algorithm of sine truncated Lomax distribution with application |
| title_short | Bayesian inference using MCMC algorithm of sine truncated Lomax distribution with application |
| title_sort | bayesian inference using mcmc algorithm of sine truncated lomax distribution with application |
| url | http://dx.doi.org/10.1063/5.0172421 |
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