Approximate Bayesian computation for censored data and its application to reliability assessment

Kristin McCullough and Nader Ebrahimi

IISE Transactions, 2018, vol. 50, issue 5, 419-430

Abstract: Approximate Bayesian Computation (ABC) refers to a family of algorithms that perform Bayesian inference under intractable likelihoods. It is widely used to perform statistical inference on complex models. In this article, we propose using ABC for reliability analysis, and we extend the scope of ABC to encompass problems that involve censored data. We are motivated by the need to assess the reliability of nanoscale components in devices. This type of analysis is difficult to perform, due to the complex structure of nanodevices and limitations imposed by fabrication processes. A consequence is that failure data often include a high proportion of censored observations. We demonstrate that our proposed ABC algorithms perform well and produce accurate parameter estimates in this setting.

Date: 2018
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DOI: 10.1080/24725854.2017.1412091

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