 Big Data? Statistical Process Control Can Help!Peihua Qiu The American Statistician, 2020, vol. 74, issue 4, 329-344 Abstract: “Big data” is a buzzword these days due to an enormous amount of data-rich applications in different industries and research projects. In practice, big data often take the form of data streams in the sense that new batches of data keep being collected over time. One fundamental research problem when analyzing big data in a given application is to monitor the underlying sequential process of the observed data to see whether it is longitudinally stable, or how its distribution changes over time. To monitor a sequential process, one major statistical tool is the statistical process control (SPC) charts, which have been developed and used mainly for monitoring production lines in the manufacturing industries during the past several decades. With many new and versatile SPC methods developed in the recent research, it is our belief that SPC can become a powerful tool for handling many big data applications that are beyond the production line monitoring. In this article, we introduce some recent SPC methods, and discuss their potential to solve some big data problems. Certain challenges in the interface between the current SPC research and some big data applications are also discussed. Date: 2020 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:taf:amstat:v:74:y:2020:i:4:p:329-344 Ordering information: This journal article can be ordered from DOI: 10.1080/00031305.2019.1700163 Access Statistics for this article The American Statistician is currently edited by Eric Sampson More articles in The American Statistician from Taylor & Francis Journals |
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