On the approximation of sum of lognormal for correlated variates and implementation.
In probabilistic modeling across engineering, finance, and telecommunications, sums of lognormal random variables frequently occur, yet no closed-form expression exists for their distribution. This study systematically evaluates three approximation methods-Wilkinson (W), Schwartz & Yeh (SY), and...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0325647 |
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| _version_ | 1849473496569085952 |
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| author | Asyraf Nadia Mohd Yunus Nora Muda Abdul Rahman Othman Sonia Aïssa |
| author_facet | Asyraf Nadia Mohd Yunus Nora Muda Abdul Rahman Othman Sonia Aïssa |
| author_sort | Asyraf Nadia Mohd Yunus |
| collection | DOAJ |
| description | In probabilistic modeling across engineering, finance, and telecommunications, sums of lognormal random variables frequently occur, yet no closed-form expression exists for their distribution. This study systematically evaluates three approximation methods-Wilkinson (W), Schwartz & Yeh (SY), and Inverse (I)-for correlated lognormal variates across varying sample sizes and correlation structures. Using Monte Carlo simulations with 5, 15, 25, and 30 samples and correlation coefficients of 0.3, 0.6, and 0.9, we compared Type I error rates through Anderson-Darling goodness-of-fit tests. Our findings demonstrate that the Wilkinson approximation consistently outperforms the other methods for correlated variates, exhibiting the lowest Type I error rates across all tested scenarios. This contradicts some previous findings in telecommunications literature where SY was preferred. We validated these results using real-world datasets from engineering (fatigue life of ball bearings) and finance (stock price correlations), confirming the Wilkinson approximation's superior performance through probability density function comparisons. This research provides practical guidance for selecting appropriate approximation methods when modeling correlated lognormal sums in diverse applications. |
| format | Article |
| id | doaj-art-e0cc908c1ec048589f826d194b0f748b |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-e0cc908c1ec048589f826d194b0f748b2025-08-20T03:24:07ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01206e032564710.1371/journal.pone.0325647On the approximation of sum of lognormal for correlated variates and implementation.Asyraf Nadia Mohd YunusNora MudaAbdul Rahman OthmanSonia AïssaIn probabilistic modeling across engineering, finance, and telecommunications, sums of lognormal random variables frequently occur, yet no closed-form expression exists for their distribution. This study systematically evaluates three approximation methods-Wilkinson (W), Schwartz & Yeh (SY), and Inverse (I)-for correlated lognormal variates across varying sample sizes and correlation structures. Using Monte Carlo simulations with 5, 15, 25, and 30 samples and correlation coefficients of 0.3, 0.6, and 0.9, we compared Type I error rates through Anderson-Darling goodness-of-fit tests. Our findings demonstrate that the Wilkinson approximation consistently outperforms the other methods for correlated variates, exhibiting the lowest Type I error rates across all tested scenarios. This contradicts some previous findings in telecommunications literature where SY was preferred. We validated these results using real-world datasets from engineering (fatigue life of ball bearings) and finance (stock price correlations), confirming the Wilkinson approximation's superior performance through probability density function comparisons. This research provides practical guidance for selecting appropriate approximation methods when modeling correlated lognormal sums in diverse applications.https://doi.org/10.1371/journal.pone.0325647 |
| spellingShingle | Asyraf Nadia Mohd Yunus Nora Muda Abdul Rahman Othman Sonia Aïssa On the approximation of sum of lognormal for correlated variates and implementation. PLoS ONE |
| title | On the approximation of sum of lognormal for correlated variates and implementation. |
| title_full | On the approximation of sum of lognormal for correlated variates and implementation. |
| title_fullStr | On the approximation of sum of lognormal for correlated variates and implementation. |
| title_full_unstemmed | On the approximation of sum of lognormal for correlated variates and implementation. |
| title_short | On the approximation of sum of lognormal for correlated variates and implementation. |
| title_sort | on the approximation of sum of lognormal for correlated variates and implementation |
| url | https://doi.org/10.1371/journal.pone.0325647 |
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