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Accelerated failure time analysis for industrial life modeling in presence of unknown dependent and independent censoring.

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Title: Accelerated failure time analysis for industrial life modeling in presence of unknown dependent and independent censoring.
Authors: Wilke, Ralf A.1 (AUTHOR) rw.eco@cbs.dk, Lo, Simon M. S.2 (AUTHOR)
Source: Quality Engineering. 2025, Vol. 37 Issue 4, p571-582. 12p.
Subjects: Failure time data analysis, Statistical reliability, Empirical research, Engineering reliability theory, Statistical models, Prediction models
Abstract: Industrial lifetime testing is one of the key procedures for industrial engineers to assess the quality of products or materials. Reliability analysis is hampered by data incompleteness resulting from multiple failure types, with only the first occurring failure being observable. This leads to major uncertainties about the fitted failure probabilities unless the model satisfies some restrictions that are often difficult to verify. This article contributes to the reliability literature by showing that state-of-the-art statistical models under weak parametric assumptions give informative estimates of failure probabilities. We introduce a new semiparametric bootstrap-based model selection test that allows for testing the validity of these restrictions. Our approach supports the engineer in crafting a parametric model based on data that gives informative results. An empirical analysis of aircraft radio lifetimes demonstrates the estimation of critical model components under various model specifications. The model selection test guides the engineer to select the model with the best fit. We illustrate the practical relevance of data-driven bias reduction techniques for models with dependent censoring. [ABSTRACT FROM AUTHOR]
Copyright of Quality Engineering is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. 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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  Data: Accelerated failure time analysis for industrial life modeling in presence of unknown dependent and independent censoring.
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  Data: <searchLink fieldCode="JN" term="%22Quality+Engineering%22">Quality Engineering</searchLink>. 2025, Vol. 37 Issue 4, p571-582. 12p.
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  Data: <searchLink fieldCode="DE" term="%22Failure+time+data+analysis%22">Failure time data analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+reliability%22">Statistical reliability</searchLink><br /><searchLink fieldCode="DE" term="%22Empirical+research%22">Empirical research</searchLink><br /><searchLink fieldCode="DE" term="%22Engineering+reliability+theory%22">Engineering reliability theory</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+models%22">Statistical models</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink>
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  Data: Industrial lifetime testing is one of the key procedures for industrial engineers to assess the quality of products or materials. Reliability analysis is hampered by data incompleteness resulting from multiple failure types, with only the first occurring failure being observable. This leads to major uncertainties about the fitted failure probabilities unless the model satisfies some restrictions that are often difficult to verify. This article contributes to the reliability literature by showing that state-of-the-art statistical models under weak parametric assumptions give informative estimates of failure probabilities. We introduce a new semiparametric bootstrap-based model selection test that allows for testing the validity of these restrictions. Our approach supports the engineer in crafting a parametric model based on data that gives informative results. An empirical analysis of aircraft radio lifetimes demonstrates the estimation of critical model components under various model specifications. The model selection test guides the engineer to select the model with the best fit. We illustrate the practical relevance of data-driven bias reduction techniques for models with dependent censoring. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Quality Engineering is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. 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.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/08982112.2025.2462111
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      – Code: eng
        Text: English
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        PageCount: 12
        StartPage: 571
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      – SubjectFull: Failure time data analysis
        Type: general
      – SubjectFull: Statistical reliability
        Type: general
      – SubjectFull: Empirical research
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      – SubjectFull: Engineering reliability theory
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      – SubjectFull: Statistical models
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      – SubjectFull: Prediction models
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              Text: 2025
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