A New Lifetime Distribution: Exponentiated Exponential-Pareto-HalfNormal Mixture Model for Biomedical Applications
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866916787470204928 |
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| author | Hassan, Oriyomi Ahmad Maradesa, Aisha Tunrayo Alabi, Abdulazeez Toyosi Salam, Oyejide Surajudeen Busari, Ajani Famotire, Akinwale Victor Afolabi, Habeeb Abiodun Adeleke, Solomon Akomolafe, Abayomi Ayodele |
| author_facet | Hassan, Oriyomi Ahmad Maradesa, Aisha Tunrayo Alabi, Abdulazeez Toyosi Salam, Oyejide Surajudeen Busari, Ajani Famotire, Akinwale Victor Afolabi, Habeeb Abiodun Adeleke, Solomon Akomolafe, Abayomi Ayodele |
| contents | This study introduces the Exponentiated-Exponential-Pareto-Half Normal Mixture Distribution (EEPHND), a novel hybrid model developed to overcome the limitations of classical distributions in modeling complex real-world data. By compounding the Exponentiated-Exponential-Pareto (EEP) and Half-Normal distributions through a mixture mechanism, EEPHND effectively captures both early-time symmetry and long-tail behavior, features which are commonly observed in survival and reliability data. The model offers closed-form expressions for its probability density, cumulative distribution, survival and hazard functions, moments, and reliability metrics, ensuring analytical traceability and interpretability in the presence of censoring and heterogeneous risk dynamics. When applied to a real-world lung cancer dataset, EEPHND outperformed competing models in both goodness-of-fit and predictive accuracy, achieving a Concordance Index (CI) of 0.9997. These results highlight its potential as a flexible and powerful tool for survival analysis and biomedical engineering. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_08313 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A New Lifetime Distribution: Exponentiated Exponential-Pareto-HalfNormal Mixture Model for Biomedical Applications Hassan, Oriyomi Ahmad Maradesa, Aisha Tunrayo Alabi, Abdulazeez Toyosi Salam, Oyejide Surajudeen Busari, Ajani Famotire, Akinwale Victor Afolabi, Habeeb Abiodun Adeleke, Solomon Akomolafe, Abayomi Ayodele Applications Methodology This study introduces the Exponentiated-Exponential-Pareto-Half Normal Mixture Distribution (EEPHND), a novel hybrid model developed to overcome the limitations of classical distributions in modeling complex real-world data. By compounding the Exponentiated-Exponential-Pareto (EEP) and Half-Normal distributions through a mixture mechanism, EEPHND effectively captures both early-time symmetry and long-tail behavior, features which are commonly observed in survival and reliability data. The model offers closed-form expressions for its probability density, cumulative distribution, survival and hazard functions, moments, and reliability metrics, ensuring analytical traceability and interpretability in the presence of censoring and heterogeneous risk dynamics. When applied to a real-world lung cancer dataset, EEPHND outperformed competing models in both goodness-of-fit and predictive accuracy, achieving a Concordance Index (CI) of 0.9997. These results highlight its potential as a flexible and powerful tool for survival analysis and biomedical engineering. |
| title | A New Lifetime Distribution: Exponentiated Exponential-Pareto-HalfNormal Mixture Model for Biomedical Applications |
| topic | Applications Methodology |
| url | https://arxiv.org/abs/2506.08313 |