Subdata selection based on orthogonal array for big data
Min Ren and
Sheng-Li Zhao
Communications in Statistics - Theory and Methods, 2023, vol. 52, issue 15, 5483-5501
Abstract:
Many branches of contemporary science are generating large amounts of data. Due to the limitation of calculation time and cost, traditional statistical methods are no longer applicable to large data sets. For a very large data set containing N points, an effective method is to extract n (n
Date: 2023
References: Add references at CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2021.2012196 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:lstaxx:v:52:y:2023:i:15:p:5483-5501
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20
DOI: 10.1080/03610926.2021.2012196
Access Statistics for this article
Communications in Statistics - Theory and Methods is currently edited by Debbie Iscoe
More articles in Communications in Statistics - Theory and Methods from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().