EconPapers    
Economics at your fingertips  
 

Optimal Poisson subsampling decorrelated score for high-dimensional generalized linear models

Junhao Shan and Lei Wang

Journal of Applied Statistics, 2024, vol. 51, issue 14, 2719-2743

Abstract: For high-dimensional generalized linear models (GLMs) with massive data, this paper investigates a unified optimal Poisson subsampling scheme to conduct estimation and inference for prespecified low-dimensional partition of the whole parameter. A Poisson subsampling decorrelated score function is proposed such that the adverse effect of the less accurate nuisance parameter estimation with slow convergence rate can be mitigated. The resultant Poisson subsample estimator is proved to enjoy consistency and asymptotic normality, and a more general optimal subsampling criterion including A- and L-optimality criteria is formulated to improve estimation efficiency. We also propose a two-step algorithm for implementation and discuss some practical issues. The satisfactory performance of our method is validated through simulation studies and a real dataset.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/02664763.2024.2315467 (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:japsta:v:51:y:2024:i:14:p:2719-2743

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CJAS20

DOI: 10.1080/02664763.2024.2315467

Access Statistics for this article

Journal of Applied Statistics is currently edited by Robert Aykroyd

More articles in Journal of Applied Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().

 
Page updated 2025-03-20
Handle: RePEc:taf:japsta:v:51:y:2024:i:14:p:2719-2743