SUPERVISED HOMOGENEITY FUSION: A COMBINATORIAL APPROACH.

ABSTRACT: Fusing regression coefficients into homogeneous groups can unveil those coefficients that share a common value within each group. Such groupwise homogeneity reduces the intrinsic dimension of the parameter space and unleashes sharper statistical accuracy. We propose and investigate a new combinatorial grouping approach called L0 -Fusion that is amenable to mixed integer optimization (MIO). On the statistical aspect, we identify a fundamental quantity called MSE grouping sensitivity that underpins the difficulty of recovering the true groups. We show that L0 -Fusion achieves grouping consistency under the weakest possible requirement of the grouping sensitivity: if this requirement is violated, then the minimax risk of group misspecification will fail to converge to zero. Moreover, w

SUBMITTER: Wang W 

PROVIDER: S-EPMC12327361 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature