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Multi-population sufficient dimension reduction
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DOI：10.1016/j.csda.2025.108321.png)
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Abstract
En 中文
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.
Keywords:
Fusion-refinement procedure
Multiple population
Partial central subspace
Sliced inverse regression
Sufficient dimension reduction
Journal
C
IF:
1.6
Papers:
43
Citations:
0
