Bayesian estimation of a power law process with incomplete data

• 作 者: HU Junming HUANG Hongzhong LI Yanfeng
• 机 构: School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of
• 出 处: 系统工程与电子技术（英文版）
• 发表日期: 2021年1期
• 关键词: incomplete data power law process Bayesian infer-ence order statistics repairabl

Due to the simplicity and flexibility of the power law process, it is widely used to model the failures of repairable sys-tems. Although statistical inference on the parameters of the power law process has been well developed, numerous studies largely depend on complete failure data. A few methods on in-complete data are reported to process such data, but they are limited to their specific cases, especially to that where missing data occur at the early stage of the failures. No framework to handle generic scenarios is available. To overcome this problem, from the point of view of order statistics, the statistical inference of the power law process with incomplete data is established in this paper. The theoretical derivation is carried out and the case studies demonstrate and verify the proposed method. Order statistics offer an alternative to the statistical inference of the power law process with incomplete data as they can reformulate current studies on the left censored failure data and interval cen-sored data in a unified framework. The results show that the pro-posed method has more flexibility and more applicability.