Modelling Zero-Truncated Count Data Using the Poisson-Prakaamy Distribution.
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| Title: | Modelling Zero-Truncated Count Data Using the Poisson-Prakaamy Distribution. |
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| Authors: | Wongprachan, Ratchaneewan1 ratchaneewan@mju.ac.th |
| Source: | IAENG International Journal of Applied Mathematics. Jul2026, Vol. 56 Issue 7, p2644-2656. 13p. |
| Subjects: | Distribution (Probability theory), Statistical models, Parameter estimation, Maximum likelihood statistics, Monte Carlo method |
| Abstract: | This study introduces the zerotruncated Poisson-Prakaamy (ZTPP) distribution, a new one-parameter probability model for zerotruncated count data. The ZTPP is formulated as a zero-truncated extension of the Poisson-Prakaamy distribution and provides a unified framework capable of modelling underdispersion, equidispersion, and overdispersion. Closed-form expressions for the first four moments are derived, and maximum likelihood estimation procedures are developed for parameter inference. A Monte Carlo simulation study across a range of sample sizes and parameter settings confirms the consistency of the estimator and demonstrates improved estimation accuracy as the sample size increases. Applications to real datasets from healthcare, cytogenetics, ecology, and insurance illustrate the practical applicability of the proposed model. Comparative analyses with existing zero-truncated models, based on chi-square goodness-of-fit tests, information criteria (AIC and BIC), and root mean square error, indicate that the ZTPP model provides a competitive or improved fit across several datasets. Overall, these results suggest that the ZTPP distribution offers a flexible and useful framework for modelling heterogeneous zero-truncated count data for a variety of scientific applications. [ABSTRACT FROM AUTHOR] |
| Copyright of IAENG International Journal of Applied Mathematics is the property of International Association of Engineers (IAENG) 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 |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 195026897 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Modelling Zero-Truncated Count Data Using the Poisson-Prakaamy Distribution. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wongprachan%2C+Ratchaneewan%22">Wongprachan, Ratchaneewan</searchLink><relatesTo>1</relatesTo><i> ratchaneewan@mju.ac.th</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IAENG+International+Journal+of+Applied+Mathematics%22">IAENG International Journal of Applied Mathematics</searchLink>. Jul2026, Vol. 56 Issue 7, p2644-2656. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Distribution+%28Probability+theory%29%22">Distribution (Probability theory)</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+models%22">Statistical models</searchLink><br /><searchLink fieldCode="DE" term="%22Parameter+estimation%22">Parameter estimation</searchLink><br /><searchLink fieldCode="DE" term="%22Maximum+likelihood+statistics%22">Maximum likelihood statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Monte+Carlo+method%22">Monte Carlo method</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This study introduces the zerotruncated Poisson-Prakaamy (ZTPP) distribution, a new one-parameter probability model for zerotruncated count data. The ZTPP is formulated as a zero-truncated extension of the Poisson-Prakaamy distribution and provides a unified framework capable of modelling underdispersion, equidispersion, and overdispersion. Closed-form expressions for the first four moments are derived, and maximum likelihood estimation procedures are developed for parameter inference. A Monte Carlo simulation study across a range of sample sizes and parameter settings confirms the consistency of the estimator and demonstrates improved estimation accuracy as the sample size increases. Applications to real datasets from healthcare, cytogenetics, ecology, and insurance illustrate the practical applicability of the proposed model. Comparative analyses with existing zero-truncated models, based on chi-square goodness-of-fit tests, information criteria (AIC and BIC), and root mean square error, indicate that the ZTPP model provides a competitive or improved fit across several datasets. Overall, these results suggest that the ZTPP distribution offers a flexible and useful framework for modelling heterogeneous zero-truncated count data for a variety of scientific applications. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IAENG International Journal of Applied Mathematics is the property of International Association of Engineers (IAENG) 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: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 2644 Subjects: – SubjectFull: Distribution (Probability theory) Type: general – SubjectFull: Statistical models Type: general – SubjectFull: Parameter estimation Type: general – SubjectFull: Maximum likelihood statistics Type: general – SubjectFull: Monte Carlo method Type: general Titles: – TitleFull: Modelling Zero-Truncated Count Data Using the Poisson-Prakaamy Distribution. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wongprachan, Ratchaneewan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 19929978 Numbering: – Type: volume Value: 56 – Type: issue Value: 7 Titles: – TitleFull: IAENG International Journal of Applied Mathematics Type: main |
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