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Bayesian and non-Bayesian inference for the compound Poisson log-normal model with application in finance.
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- Author(s): Almuqrin, Muqrin A.
- Source:
Alexandria Engineering Journal; Mar2024, Vol. 90, p24-43, 20p- Subject Terms:
- Source:
- Additional Information
- Abstract: In this article, we described a novel class of distributions that can be analyzed in complex and skewed real data sets. We called the proposed distribution as compound Poisson log-normal model, and it obtained by compounding the truncated Poisson and log-normal random variables. The baseline distribution can be obtained as a special case. The article derives different fundamental statistical properties of the proposed model, such as the quantile function, moments, moment-generating function, and information measures. Further, the estimation of unknown parameters is derived using classical and Bayesian procedures via numerous loss functions. The associated confidence intervals can also be obtained from approximate and bootstrap methods. The potential of the proposed estimators is computed using experiment simulation studies to show the best and most efficient method of estimation. Finally, we illustrate two financial data sets to see the effectiveness of the suggested model among other proposed distributions. [ABSTRACT FROM AUTHOR]
- Abstract: Copyright of Alexandria Engineering Journal is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. 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.)
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