Robust inference and model selection for data from one-shot devices under cyclic accelerated life-tests with an application to a test of CSP solder joints

We introduce here a new family of divergence-based estimators in this work for predicting the lifetimes of one-shot devices subjected to cyclic Accelerated Life-Tests (ALTs). This family, which includes the maximum likelihood estimator (MLE) as a special case, offers a robust alternative to traditional inferential procedures. We also present a family of divergence-based model selection criteria. A simulation study and a numerical example illustrate the advantages of these estimators and the robust inferential methods based on them.

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Publication Details

Journal
Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability
Published
2025-02-10
DOI
https://doi.org/10.1177/1748006x251314506
Citations
3
Primary Topic
Probabilistic and Robust Engineering Design
Type
article
Field-Weighted Citation Impact
4.20
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article

Robust inference and model selection for data from one-shot devices under cyclic accelerated life-tests with an application to a test of CSP solder joints

Elena Castilla, N. Balakrishnan
3 citations
Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability
Probabilistic and Robust Engineering Design
4.20
article

Robust inference and model selection for data from one-shot devices under cyclic accelerated life-tests with an application to a test of CSP solder joints

Elena Castilla, N. Balakrishnan
article en
3 citations

Abstract

We introduce here a new family of divergence-based estimators in this work for predicting the lifetimes of one-shot devices subjected to cyclic Accelerated Life-Tests (ALTs). This family, which includes the maximum likelihood estimator (MLE) as a special case, offers a robust alternative to traditional inferential procedures. We also present a family of divergence-based model selection criteria. A simulation study and a numerical example illustrate the advantages of these estimators and the robust inferential methods based on them.

Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability
Universidad Rey Juan Carlos (ES), McMaster University (CA)
Openalex Percentile: Top 6%
Probabilistic and Robust Engineering Design
4.20
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