Estimation of Sine Inverse Exponential Model under Censored Schemes
In this article, we introduce a new one-parameter model, which is named sine inverted exponential (SIE) distribution. The SIE distribution is a new extension of the inverse exponential (IE) distribution. The SIE distribution aims to provide the SIE model for data-fitting purposes. The SIE distributi...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2021/7330385 |
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| author | M. Shrahili I. Elbatal Waleed Almutiry Mohammed Elgarhy |
| author_facet | M. Shrahili I. Elbatal Waleed Almutiry Mohammed Elgarhy |
| author_sort | M. Shrahili |
| collection | DOAJ |
| description | In this article, we introduce a new one-parameter model, which is named sine inverted exponential (SIE) distribution. The SIE distribution is a new extension of the inverse exponential (IE) distribution. The SIE distribution aims to provide the SIE model for data-fitting purposes. The SIE distribution is more flexible than the inverted exponential (IE) model, and it has many applications in physics, medicine, engineering, nanophysics, and nanoscience. The density function (PDFu) of SIE distribution can be unimodel shape and right skewed shape. The hazard rate function (HRFu) of SIE distribution can be J-shaped and increasing shaped. We investigated some fundamental statistical properties such as quantile function (QFu), moments (Mo), moment generating function (MGFu), incomplete moments (ICMo), conditional moments (CMo), and the SIE distribution parameters were estimated using the maximum likelihood (ML) method for estimation under censored samples (CS). Finally, the numerical results were investigated to evaluate the flexibility of the new model. The SIE distribution and the IE distribution are compared using two real datasets. The numerical results show the superiority of the SIE distribution. |
| format | Article |
| id | doaj-art-2723318a644444acbf0166001b98d247 |
| institution | Kabale University |
| issn | 2314-4629 2314-4785 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Mathematics |
| spelling | doaj-art-2723318a644444acbf0166001b98d2472025-02-03T01:04:10ZengWileyJournal of Mathematics2314-46292314-47852021-01-01202110.1155/2021/73303857330385Estimation of Sine Inverse Exponential Model under Censored SchemesM. Shrahili0I. Elbatal1Waleed Almutiry2Mohammed Elgarhy3Department of Statistics and Operations Research, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi ArabiaDepartment of Mathematics and Statistics, College of Science Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi ArabiaDepartment of Mathematics, College of Science and Arts in Ar Rass, Qassim University, Ar Rass, Saudi ArabiaThe Higher Institute of Commercial Sciences, Al Mahalla Al Kubra 31951, Algharbia, EgyptIn this article, we introduce a new one-parameter model, which is named sine inverted exponential (SIE) distribution. The SIE distribution is a new extension of the inverse exponential (IE) distribution. The SIE distribution aims to provide the SIE model for data-fitting purposes. The SIE distribution is more flexible than the inverted exponential (IE) model, and it has many applications in physics, medicine, engineering, nanophysics, and nanoscience. The density function (PDFu) of SIE distribution can be unimodel shape and right skewed shape. The hazard rate function (HRFu) of SIE distribution can be J-shaped and increasing shaped. We investigated some fundamental statistical properties such as quantile function (QFu), moments (Mo), moment generating function (MGFu), incomplete moments (ICMo), conditional moments (CMo), and the SIE distribution parameters were estimated using the maximum likelihood (ML) method for estimation under censored samples (CS). Finally, the numerical results were investigated to evaluate the flexibility of the new model. The SIE distribution and the IE distribution are compared using two real datasets. The numerical results show the superiority of the SIE distribution.http://dx.doi.org/10.1155/2021/7330385 |
| spellingShingle | M. Shrahili I. Elbatal Waleed Almutiry Mohammed Elgarhy Estimation of Sine Inverse Exponential Model under Censored Schemes Journal of Mathematics |
| title | Estimation of Sine Inverse Exponential Model under Censored Schemes |
| title_full | Estimation of Sine Inverse Exponential Model under Censored Schemes |
| title_fullStr | Estimation of Sine Inverse Exponential Model under Censored Schemes |
| title_full_unstemmed | Estimation of Sine Inverse Exponential Model under Censored Schemes |
| title_short | Estimation of Sine Inverse Exponential Model under Censored Schemes |
| title_sort | estimation of sine inverse exponential model under censored schemes |
| url | http://dx.doi.org/10.1155/2021/7330385 |
| work_keys_str_mv | AT mshrahili estimationofsineinverseexponentialmodelundercensoredschemes AT ielbatal estimationofsineinverseexponentialmodelundercensoredschemes AT waleedalmutiry estimationofsineinverseexponentialmodelundercensoredschemes AT mohammedelgarhy estimationofsineinverseexponentialmodelundercensoredschemes |