Semiparametric learning from open-set label shift data

Clicks: 12
ID: 333415
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #150 of 153 articles by views in Journal of the Royal Statistical Society Series B (Statistical Methodology)

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 153 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Abstract We study the open-set label shift problem, where the test data may include a novel class absent from the training data. This setting is challenging because both the class proportions and the distribution of the novel class are not identifiable without extra assumptions. Existing approaches often rely on restrictive separability conditions, prior knowledge, or computationally infeasible procedures, and some may lack theoretical guarantees. We propose a semiparametric density ratio model framework that ensures identifiability while allowing overlap between novel and known classes. Within this framework, we develop maximum empirical likelihood estimators and confidence intervals for class proportions, establish their asymptotic validity, and design a stable Expectation–Maximization algorithm for computation. We also develop a goodness-of-fit test for model assessment and an approximately optimal classifier with theoretical guarantees. Simulations and a real-data application confirm that our methods improve both estimation accuracy and classification performance compared with existing approaches.
Reference Key
openalex_W4417081453 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Siyan Liu, Yukun Liu, Qinglong Tian, Pengfei Li, Jing Qin
Are you one of these authors? Claim your profile
Journal Journal of the Royal Statistical Society Series B (Statistical Methodology)
Year 2026
DOI
10.1093/jrsssb/qkag134
URL
https://doi.org/10.1093/jrsssb/qkag134
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.