Optimal distributed Poisson subsampling for modal regression with massive data

Cheng Li, Ruihan Luo and Jian-Feng Yang ()
Additional contact information
Cheng Li: LPMC & KLMDASR, Nankai University
Ruihan Luo: CSSC Systems Engineering Research Institute
Jian-Feng Yang: LPMC & KLMDASR, Nankai University

Statistical Papers, 2025, vol. 66, issue 5, No 20, 32 pages

Abstract: Abstract Modern statistical analysis often grapples with the challenge of limited computational resources when handling large datasets. Subsampling, a widely adopted solution, is particularly effective in reducing computational burden while maintaining estimation efficiency. However, subsampling with replacement faces memory constraint issues; if the data volume is so large that subsampling probabilities cannot be loaded into memory all at once, it becomes infeasible to implement. To tackle this problem, in this paper, we propose an optimal Poisson subsampling method in the context of modal regression with massive data. A practical two-step algorithm based on the weighted modal expectation-maximization iteration is proposed, and the consistency and asymptotic normality of the resulting estimator are investigated. Additionally, a communication-efficient distributed modal regression method via optimal Poisson subsampling (CDMROS) is developed, with the asymptotic properties of the resultant estimator established. Numerical experiments on both simulated and real data sets are conducted to demonstrate the superior performance of proposed methods.

Keywords: Distributed inference; Massive data; Modal regression; Optimality criteria; Poisson subsampling (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00362-025-01747-1 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:66:y:2025:i:5:d:10.1007_s00362-025-01747-1

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

DOI: 10.1007/s00362-025-01747-1

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 ().