{"title":"Non-parametric change-point approach for monitoring shifts in process location and variability","authors":"R. Afolabi, P. A. Osanaiye, O. Akpa","doi":"10.1504/ijqet.2015.069234","DOIUrl":null,"url":null,"abstract":"In statistical process control, detecting if the process is in control and the position of shift in an out-of-control process are critical research problems. If the normality assumption is satisfied, work has advanced in detecting shifts in mean and/or variance. However, the normality assumption is often not satisfied in many real life situations. We suggest a non-parametric Lepage-type change-point (LCP) control chart for jointly detecting process shifts in mean and variance, under non-normality. A comparison between our proposed method and a generalised likelihood ratio (GLR)-based method was made. Process data were simulated following normal and Laplace distributions. The performances of LCP and GLR were assessed and presented, using evaluated average run lengths, under the distributions considered. The LCP competed favourably with the GLR in a normal distribution. However, LCP outperformed GLR under the heavy-tailed distribution considered. We recommend the new approach for short-run situations where the underlying distributions are usually unknown.","PeriodicalId":38209,"journal":{"name":"International Journal of Quality Engineering and Technology","volume":"23 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2015-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/ijqet.2015.069234","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Quality Engineering and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1504/ijqet.2015.069234","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Engineering","Score":null,"Total":0}
引用次数: 4
Abstract
In statistical process control, detecting if the process is in control and the position of shift in an out-of-control process are critical research problems. If the normality assumption is satisfied, work has advanced in detecting shifts in mean and/or variance. However, the normality assumption is often not satisfied in many real life situations. We suggest a non-parametric Lepage-type change-point (LCP) control chart for jointly detecting process shifts in mean and variance, under non-normality. A comparison between our proposed method and a generalised likelihood ratio (GLR)-based method was made. Process data were simulated following normal and Laplace distributions. The performances of LCP and GLR were assessed and presented, using evaluated average run lengths, under the distributions considered. The LCP competed favourably with the GLR in a normal distribution. However, LCP outperformed GLR under the heavy-tailed distribution considered. We recommend the new approach for short-run situations where the underlying distributions are usually unknown.
期刊介绍:
IJQET fosters the exchange and dissemination of research publications aimed at the latest developments in all areas of quality engineering. The thrust of this international journal is to publish original full-length articles on experimental and theoretical basic research with scholarly rigour. IJQET particularly welcomes those emerging methodologies and techniques in concise and quantitative expressions of the theoretical and practical engineering and science disciplines.