{"title":"Statistical process monitoring for vector autoregressive time series based on location-scale CUSUM method","authors":"Sangjo Lee, Sangyeol Lee","doi":"10.1080/08982112.2022.2156295","DOIUrl":null,"url":null,"abstract":"Abstract In this study, we design a monitoring method for the vector autoregressive (VAR) and structural VAR (SVAR) time series using the residual-based cumulative sum (CUSUM) control chart. The residuals are calculated with a sequentially observed testing sample and the parameter estimates obtained from a training sample. Control limits are determined asymptotically when type 1 error probability scheme is used, but average run length (ARL) is also used in our empirical study. For the SVAR time series, independent component analysis (ICA) method is applied. A simulation study and real data analysis are conducted to evaluate our method.","PeriodicalId":20846,"journal":{"name":"Quality Engineering","volume":"35 1","pages":"493 - 518"},"PeriodicalIF":1.3000,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Quality Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/08982112.2022.2156295","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
引用次数: 1
Abstract
Abstract In this study, we design a monitoring method for the vector autoregressive (VAR) and structural VAR (SVAR) time series using the residual-based cumulative sum (CUSUM) control chart. The residuals are calculated with a sequentially observed testing sample and the parameter estimates obtained from a training sample. Control limits are determined asymptotically when type 1 error probability scheme is used, but average run length (ARL) is also used in our empirical study. For the SVAR time series, independent component analysis (ICA) method is applied. A simulation study and real data analysis are conducted to evaluate our method.
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