{"title":"ANI和ARL无偏几何图和CCCG控制图的比较研究","authors":"N. Kumar, R. Singh","doi":"10.1080/07474946.2020.1823194","DOIUrl":null,"url":null,"abstract":"Abstract Geometric charts have an important role in monitoring fraction nonconforming in high-yield processes where the rate of nonconforming is very low, say, parts per million. Currently, the average number of inspected items (ANI) to give an out-of-control (OOC) signal is preferred to the average run length (ARL) in designing and evaluating geometric charts. The ANI carries more information than the ARL because the former considers the number of inspected units contained in each charting point until a signal occurs. Like ARL, the ANI function possesses bias, which results that the chart requiring more items to be inspected to detect an OOC signal rather than a false alarm. In this article, an ANI-unbiased geometric chart is proposed and its performance is compared with the existing ARL-unbiased chart. The study shows that neither is better than the other for all shifts in the process parameter. The study is also extended to the CCCG chart where a group of samples is inspected instead of individual items.","PeriodicalId":48879,"journal":{"name":"Sequential Analysis-Design Methods and Applications","volume":"39 1","pages":"399 - 416"},"PeriodicalIF":0.6000,"publicationDate":"2020-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/07474946.2020.1823194","citationCount":"1","resultStr":"{\"title\":\"A comparative study of ANI- and ARL-unbiased geometric and CCCG control charts\",\"authors\":\"N. Kumar, R. Singh\",\"doi\":\"10.1080/07474946.2020.1823194\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract Geometric charts have an important role in monitoring fraction nonconforming in high-yield processes where the rate of nonconforming is very low, say, parts per million. Currently, the average number of inspected items (ANI) to give an out-of-control (OOC) signal is preferred to the average run length (ARL) in designing and evaluating geometric charts. The ANI carries more information than the ARL because the former considers the number of inspected units contained in each charting point until a signal occurs. Like ARL, the ANI function possesses bias, which results that the chart requiring more items to be inspected to detect an OOC signal rather than a false alarm. In this article, an ANI-unbiased geometric chart is proposed and its performance is compared with the existing ARL-unbiased chart. The study shows that neither is better than the other for all shifts in the process parameter. The study is also extended to the CCCG chart where a group of samples is inspected instead of individual items.\",\"PeriodicalId\":48879,\"journal\":{\"name\":\"Sequential Analysis-Design Methods and Applications\",\"volume\":\"39 1\",\"pages\":\"399 - 416\"},\"PeriodicalIF\":0.6000,\"publicationDate\":\"2020-07-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1080/07474946.2020.1823194\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Sequential Analysis-Design Methods and Applications\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://doi.org/10.1080/07474946.2020.1823194\",\"RegionNum\":4,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"STATISTICS & PROBABILITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sequential Analysis-Design Methods and Applications","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1080/07474946.2020.1823194","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
A comparative study of ANI- and ARL-unbiased geometric and CCCG control charts
Abstract Geometric charts have an important role in monitoring fraction nonconforming in high-yield processes where the rate of nonconforming is very low, say, parts per million. Currently, the average number of inspected items (ANI) to give an out-of-control (OOC) signal is preferred to the average run length (ARL) in designing and evaluating geometric charts. The ANI carries more information than the ARL because the former considers the number of inspected units contained in each charting point until a signal occurs. Like ARL, the ANI function possesses bias, which results that the chart requiring more items to be inspected to detect an OOC signal rather than a false alarm. In this article, an ANI-unbiased geometric chart is proposed and its performance is compared with the existing ARL-unbiased chart. The study shows that neither is better than the other for all shifts in the process parameter. The study is also extended to the CCCG chart where a group of samples is inspected instead of individual items.
期刊介绍:
The purpose of Sequential Analysis is to contribute to theoretical and applied aspects of sequential methodologies in all areas of statistical science. Published papers highlight the development of new and important sequential approaches.
Interdisciplinary articles that emphasize the methodology of practical value to applied researchers and statistical consultants are highly encouraged. Papers that cover contemporary areas of applications including animal abundance, bioequivalence, communication science, computer simulations, data mining, directional data, disease mapping, environmental sampling, genome, imaging, microarrays, networking, parallel processing, pest management, sonar detection, spatial statistics, tracking, and engineering are deemed especially important. Of particular value are expository review articles that critically synthesize broad-based statistical issues. Papers on case-studies are also considered. All papers are refereed.