A review on design inspired subsampling for big data

Jun Yu (), Mingyao Ai () and Zhiqiang Ye ()
Additional contact information
Jun Yu: Beijing Institute of Technology
Mingyao Ai: Peking University
Zhiqiang Ye: Peking University

Statistical Papers, 2024, vol. 65, issue 2, No 1, 467-510

Abstract: Abstract Subsampling focuses on selecting a subsample that can efficiently sketch the information of the original data in terms of statistical inference. It provides a powerful tool in big data analysis and gains the attention of data scientists in recent years. In this review, some state-of-the-art subsampling methods inspired by statistical design are summarized. Three types of designs, namely optimal design, orthogonal design, and space filling design, have shown their great potential in subsampling for different objectives. The relationships between experimental designs and the related subsampling approaches are discussed. Specifically, two major families of design inspired subsampling techniques are presented. The first aims to select a subsample in accordance with some optimal design criteria. The second tries to find a subsample that meets some design requirements, including balancing, orthogonality, and uniformity. Simulated and real data examples are provided to compare these methods empirically.

Keywords: Massive data; Optimal design; Orthogonal array; Space filling design; 62D05; 62K05; 62K86 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00362-022-01386-w Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:stpapr:v:65:y:2024:i:2:d:10.1007_s00362-022-01386-w

Ordering information: This journal article can be ordered from
http://www.springer. ... business/journal/362

DOI: 10.1007/s00362-022-01386-w

Access Statistics for this article

Statistical Papers is currently edited by C. Müller, W. Krämer and W.G. Müller

More articles in Statistical Papers from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().