Predictive ratio CUSUM (PRC): A Bayesian approach in online change point detection of short runs

作     者：Bourazas, Konstantinos Sobas, Frederic Tsiamyrtzis, Panagiotis 

作者机构：Athens Univ Econ & Business Dept Stat Athens Greece Hosp Civils Lyon Multis Hemostasis Lab Lyon France Politecn Milan Dept Mech Engn via La Masa 1 I-20156 Milan Italy 

出 版 物：《JOURNAL OF QUALITY TECHNOLOGY》 (质量技术杂志)

年 卷 期：2023年第55卷第4期

页      面：391-403页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0714[理学-统计学（可授理学、经济学学位）] 

基　　金：Research Center of Athens University of Economics and Business (RC/AUEB) Instrumentation Laboratory (IL), Bedford, Massachusetts, USA 

主　　题：control chart phase I analysis regular exponential family self-starting statistical process control and monitoring 

摘      要：The online quality monitoring of a process with low volume data is a very challenging task and the attention is most often placed in detecting when some of the underline (unknown) process parameter(s) experience a persistent shift. Self-starting methods, both in the frequentist and the Bayesian domain aim to offer a solution. Adopting the latter perspective, we propose a general closed-form Bayesian scheme, where the testing procedure is built on a memory-based control chart that relies on the cumulative ratios of sequentially updated predictive distributions. The theoretic framework can accommodate any likelihood from the regular exponential family and the use of conjugate analysis allows closed form modeling. Power priors will offer the axiomatic framework to incorporate into the model different sources of information, when available. A simulation study evaluates the performance against competitors and examines aspects of prior sensitivity. Technical details and algorithms are provided as .