A partitioned quasi-likelihood for distributed statistical inference

Guangbao Guo (), Yue Sun and Xuejun Jiang
Additional contact information
Guangbao Guo: Shandong University of Technology
Yue Sun: Shandong University of Technology
Xuejun Jiang: Southern University of Science and Technology

Computational Statistics, 2020, vol. 35, issue 4, No 3, 1577-1596

Abstract: Abstract In the big data setting, working data sets are often distributed on multiple machines. However, classical statistical methods are often developed to solve the problems of single estimation or inference. We employ a novel parallel quasi-likelihood method in generalized linear models, to make the variances between different sub-estimators relatively similar. Estimates are obtained from projection subsets of data and later combined by suitably-chosen unknown weights. We also show the proposed method to produce better asymptotic efficiency than using the simple average. Furthermore, simulation examples show that the proposed method can significantly improve statistical inference.

Keywords: Distributed statistical inference; Parallel computing; Quasi-likelihood; Projection matrix; Distributed data (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00180-020-00974-4 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:compst:v:35:y:2020:i:4:d:10.1007_s00180-020-00974-4

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/180/PS2

DOI: 10.1007/s00180-020-00974-4

Access Statistics for this article

Computational Statistics is currently edited by Wataru Sakamoto, Ricardo Cao and Jürgen Symanzik

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