Burn-in tests have been discussed extensively in the reliability literature, wherein we operate items until high degradation values are observed, which could separate the weak units from the normal ones before they get to the market. This concept is often referred to as a screening procedure, and it involves misclassification errors. Commonly, the underlying degradation process is assumed to be a Wiener or a gamma process, based on which several optimal burn-in policies have been developed in the literature. In this article, we consider the mixture inverse Gaussian process, which possesses monotone degradation paths and some interesting properties. Under this process, we present a decision rule for classifying a unit under test as normal or weak based on burn-in time and a set of cutoff points. Then, an economic cost model is used to find the optimal burn-in time and the optimal cutoff points, when the estimation of model parameters is based on an analytical method or an approximate method involving copula theory. Finally, an example of a real dataset on light amplification by stimulated emission of radiations, well known in the reliability literature, is used to illustrate the model and the inferential approach proposed here.

中文翻译：

---

使用混合逆高斯退化过程和 copula 基于一组截止点的最佳老化策略

在可靠性文献中已经广泛讨论了老化测试，其中我们运行项目直到观察到高退化值，这可以在它们进入市场之前将弱单元与正常单元分开。这个概念通常被称为筛选程序，它涉及错误分类错误。通常，潜在的退化过程被假定为 Wiener 或 gamma 过程，在此基础上，文献中已经开发了几种最佳老化策略。在本文中，我们考虑混合逆高斯过程，它具有单调退化路径和一些有趣的特性。在这个过程中，我们提出了一个决策规则，用于根据老化时间和一组截止点将被测单元分类为正常或弱。然后，当模型参数的估计基于分析方法或涉及 copula 理论的近似方法时，使用经济成本模型来寻找最佳老化时间和最佳截止点。最后，使用可靠性文献中众所周知的关于受激辐射光放大的真实数据集的示例来说明这里提出的模型和推理方法。