Abstract
A novel dimension-reduction method is introduced for multi-population data. The approach conducts a joint analysis that exploits information shared across populations while accommodating population-specific effects. Unlike partial dimension reduction methods, which identify related directions across all populations, or conditional analyses conducted independently within each population, the proposed two-step procedure leverages cross-population information to enhance estimation accuracy. The methodology is demonstrated through simulations and two real-data applications.
Recommended Citation
X. M. Wen et al., "Multi-population Sufficient Dimension Reduction," Computational Statistics and Data Analysis, vol. 217, article no. 108321, Elsevier, May 2026.
The definitive version is available at https://doi.org/10.1016/j.csda.2025.108321
Department(s)
Mathematics and Statistics
Publication Status
Full Text Access
Keywords and Phrases
Fusion-refinement procedure; Multiple population; Partial central subspace; Sliced inverse regression; Sufficient dimension reduction
International Standard Serial Number (ISSN)
0167-9473
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2026 Elsevier, All rights reserved.
Publication Date
01 May 2026
