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ADVANCED STATISTICAL APPROACH TO FAILURE DATA WITH GAMMA AND WEIBULL DISTRIBUTIONS

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Title: ADVANCED STATISTICAL APPROACH TO FAILURE DATA WITH GAMMA AND WEIBULL DISTRIBUTIONS
Authors: Vijayan S, S, Kavitha
Publisher Information: Reliability: Theory & Applications, 2025.
Publication Year: 2025
Subject Terms: Akaike information criterion, Gamma distribution, Biomedical, Probability density function, Cumulative density function, Weibull distribution, Bayesian information criterion
Description: This paper aims to systematically investigate the utility of the Gamma and Weibull distributions, focusing on their application to biomedical datasets and clarifying their mathematical and statistical properties. By analyzing lifetime data across various disciplines, the research emphasizes the effectiveness and flexibility of these distributions in capturing the complexities of biomedical data. It underscores the importance of parameters such as standard error, log-likelihood, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) in value estimation. The findings suggest that both distributions provide valuable insights into the underlying data, with practical implications for reliability engineering and failure analysis. Moreover, the study demonstrates that the Weibull distribution offers a better fit to the given data than the Gamma distribution due to its adaptability, which yields superior results. A key contribution of this study is the proposal of a model based on estimating the Conditional Weibull distribution for feature parameters, which accurately predicts a finite mixture of two-parameter Weibull distributions initially verified on datasets.
Document Type: Research
DOI: 10.24412/1932-2321-2025-182-848-854
Rights: CC BY
Accession Number: edsair.doi...........5e37b7b67ef2337f1be7589370a82ea4
Database: OpenAIRE
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  Data: ADVANCED STATISTICAL APPROACH TO FAILURE DATA WITH GAMMA AND WEIBULL DISTRIBUTIONS
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  Data: <searchLink fieldCode="AR" term="%22Vijayan+S%22">Vijayan S</searchLink><br /><searchLink fieldCode="AR" term="%22S%2C+Kavitha%22">S, Kavitha</searchLink>
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  Data: Reliability: Theory & Applications, 2025.
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  Data: 2025
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  Data: <searchLink fieldCode="DE" term="%22Akaike+information+criterion%22">Akaike information criterion</searchLink><br /><searchLink fieldCode="DE" term="%22Gamma+distribution%22">Gamma distribution</searchLink><br /><searchLink fieldCode="DE" term="%22Biomedical%22">Biomedical</searchLink><br /><searchLink fieldCode="DE" term="%22Probability+density+function%22">Probability density function</searchLink><br /><searchLink fieldCode="DE" term="%22Cumulative+density+function%22">Cumulative density function</searchLink><br /><searchLink fieldCode="DE" term="%22Weibull+distribution%22">Weibull distribution</searchLink><br /><searchLink fieldCode="DE" term="%22Bayesian+information+criterion%22">Bayesian information criterion</searchLink>
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  Data: This paper aims to systematically investigate the utility of the Gamma and Weibull distributions, focusing on their application to biomedical datasets and clarifying their mathematical and statistical properties. By analyzing lifetime data across various disciplines, the research emphasizes the effectiveness and flexibility of these distributions in capturing the complexities of biomedical data. It underscores the importance of parameters such as standard error, log-likelihood, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) in value estimation. The findings suggest that both distributions provide valuable insights into the underlying data, with practical implications for reliability engineering and failure analysis. Moreover, the study demonstrates that the Weibull distribution offers a better fit to the given data than the Gamma distribution due to its adaptability, which yields superior results. A key contribution of this study is the proposal of a model based on estimating the Conditional Weibull distribution for feature parameters, which accurately predicts a finite mixture of two-parameter Weibull distributions initially verified on datasets.
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  Data: 10.24412/1932-2321-2025-182-848-854
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  Data: CC BY
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  Data: edsair.doi...........5e37b7b67ef2337f1be7589370a82ea4
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        Value: 10.24412/1932-2321-2025-182-848-854
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    Subjects:
      – SubjectFull: Akaike information criterion
        Type: general
      – SubjectFull: Gamma distribution
        Type: general
      – SubjectFull: Biomedical
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      – SubjectFull: Probability density function
        Type: general
      – SubjectFull: Cumulative density function
        Type: general
      – SubjectFull: Weibull distribution
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      – SubjectFull: Bayesian information criterion
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      – TitleFull: ADVANCED STATISTICAL APPROACH TO FAILURE DATA WITH GAMMA AND WEIBULL DISTRIBUTIONS
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