Classical and Bayesian inference for the discrete Poisson Ramos-Louzada distribution with application to COVID-19 data
The present study is based on the derivation of a new extension of the Poisson distribution using the Ramos-Louzada distribution. Several statistical properties of the new distribution are derived including, factorial moments, moment-generating function, probability moments, skewness, kurtosis, and...
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Veröffentlicht in: | Mathematical Biosciences and Engineering 2023-08, Vol.20 (8), p.14061-14080 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | The present study is based on the derivation of a new extension of the Poisson distribution using the Ramos-Louzada distribution. Several statistical properties of the new distribution are derived including, factorial moments, moment-generating function, probability moments, skewness, kurtosis, and dispersion index. Some reliability properties are also derived. The model parameter is estimated using different classical estimation techniques. A comprehensive simulation study was used to identify the best estimation method. Bayesian estimation with a gamma prior is also utilized to estimate the parameter. Three examples were used to demonstrate the utility of the proposed model. These applications revealed that the PRL-based model outperforms certain existing competing one-parameter discrete models such as the discrete Rayleigh, Poisson, discrete inverted Topp-Leone, discrete Pareto and discrete Burr-Hatke distributions. |
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ISSN: | 1551-0018 1551-0018 |
DOI: | 10.3934/mbe.2023628 |