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. |
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| 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] |
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| Database: | Engineering Source |
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| 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] |
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| ISSN: | 08982112 |
| DOI: | 10.1080/08982112.2025.2462111 |
