The weighted Lindley-G family of probabilistic models: properties, inference, and applications to real-life data.
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| Title: | The weighted Lindley-G family of probabilistic models: properties, inference, and applications to real-life data. |
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| Authors: | Alnssyan, Badr1 (AUTHOR) b.alnssyan@qu.edu.sa, Hussein, Ekramy A.2 (AUTHOR), Alizadeh, Morad3 (AUTHOR), Afify, Ahmed Z.4 (AUTHOR) b.alnssyan@qu.edu.sa, Abdellatif, Ashraf D.5 (AUTHOR) |
| Source: | Journal of Intelligent & Fuzzy Systems. 2023, Vol. 44 Issue 5, p8071-8089. 19p. |
| Subjects: | Probabilistic databases, Distribution (Probability theory), Families, Renyi's entropy, Parameter estimation |
| Abstract: | We propose a new wider family called the weighted Lindley-G family. We derive some mathematical properties and special sub-models of the new family. We address the estimation of the model parameters by eight approaches of estimation. The estimation approaches are ranked and compared by using detailed simulations to develop a guideline for choosing the best approach for estimating the distribution parameters. The potentiality of the new family is illustrated via two applications to real-life data. It is shown that the proposed WLi-G family is more flexible as compared to some of the most cited families in the distribution theory literature such as the exponentiated-G, beta-G, transmuted-G, and alpha-power-G families under the same baseline model. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Intelligent & Fuzzy Systems is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 164008007 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: The weighted Lindley-G family of probabilistic models: properties, inference, and applications to real-life data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Alnssyan%2C+Badr%22">Alnssyan, Badr</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> b.alnssyan@qu.edu.sa</i><br /><searchLink fieldCode="AR" term="%22Hussein%2C+Ekramy+A%2E%22">Hussein, Ekramy A.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Alizadeh%2C+Morad%22">Alizadeh, Morad</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Afify%2C+Ahmed+Z%2E%22">Afify, Ahmed Z.</searchLink><relatesTo>4</relatesTo> (AUTHOR)<i> b.alnssyan@qu.edu.sa</i><br /><searchLink fieldCode="AR" term="%22Abdellatif%2C+Ashraf+D%2E%22">Abdellatif, Ashraf D.</searchLink><relatesTo>5</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Intelligent+%26+Fuzzy+Systems%22">Journal of Intelligent & Fuzzy Systems</searchLink>. 2023, Vol. 44 Issue 5, p8071-8089. 19p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Probabilistic+databases%22">Probabilistic databases</searchLink><br /><searchLink fieldCode="DE" term="%22Distribution+%28Probability+theory%29%22">Distribution (Probability theory)</searchLink><br /><searchLink fieldCode="DE" term="%22Families%22">Families</searchLink><br /><searchLink fieldCode="DE" term="%22Renyi's+entropy%22">Renyi's entropy</searchLink><br /><searchLink fieldCode="DE" term="%22Parameter+estimation%22">Parameter estimation</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: We propose a new wider family called the weighted Lindley-G family. We derive some mathematical properties and special sub-models of the new family. We address the estimation of the model parameters by eight approaches of estimation. The estimation approaches are ranked and compared by using detailed simulations to develop a guideline for choosing the best approach for estimating the distribution parameters. The potentiality of the new family is illustrated via two applications to real-life data. It is shown that the proposed WLi-G family is more flexible as compared to some of the most cited families in the distribution theory literature such as the exponentiated-G, beta-G, transmuted-G, and alpha-power-G families under the same baseline model. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Intelligent & Fuzzy Systems is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3233/JIFS-222758 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 19 StartPage: 8071 Subjects: – SubjectFull: Probabilistic databases Type: general – SubjectFull: Distribution (Probability theory) Type: general – SubjectFull: Families Type: general – SubjectFull: Renyi's entropy Type: general – SubjectFull: Parameter estimation Type: general Titles: – TitleFull: The weighted Lindley-G family of probabilistic models: properties, inference, and applications to real-life data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Alnssyan, Badr – PersonEntity: Name: NameFull: Hussein, Ekramy A. – PersonEntity: Name: NameFull: Alizadeh, Morad – PersonEntity: Name: NameFull: Afify, Ahmed Z. – PersonEntity: Name: NameFull: Abdellatif, Ashraf D. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: 2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 10641246 Numbering: – Type: volume Value: 44 – Type: issue Value: 5 Titles: – TitleFull: Journal of Intelligent & Fuzzy Systems Type: main |
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