Sen A, Fries A
Department of Mathematical Sciences, Oakland University, Rochester, MI 48309, USA.
Lifetime Data Anal. 1998;4(1):65-81. doi: 10.1023/a:1009656109978.
We describe a family of related discrete reliability-growth methodologies potentially applicable to one-shot systems undergoing a test-analyze-and-fix development process. The common feature shared by the models is their connection to Duane's renowned learning-curve property. The major difference, however, lies in their applicability in the context of two intrinsically different sampling schemes. For each model, a summary of the statistical properties of various estimators of the parameters as well as the reliability of the system, are reported. For purposes of assessing model misspecification, a particular text execution scenario conforming to a inverse sampling scheme is adopted. In reliability applications, it is not an uncommon practice to borrow inference results from models which are inappropriate in this setting. A detailed study of the potential impact of such misspecification on the estimation of system reliability is presented.
我们描述了一族相关的离散可靠性增长方法，这些方法可能适用于经历测试-分析-修复开发过程的一次性系统。这些模型的共同特征是它们与杜安著名的学习曲线特性相关联。然而，主要区别在于它们在两种本质上不同的抽样方案背景下的适用性。对于每个模型，报告了参数的各种估计量以及系统可靠性的统计特性总结。为了评估模型误设，采用了一种符合逆抽样方案的特定文本执行场景。在可靠性应用中，从不合适的模型借用推断结果并非罕见的做法。本文详细研究了这种误设对系统可靠性估计的潜在影响。
