Adaptive Multi-Prior Lasso for High-Dimensional Generalized Linear Models
高维广义线性模型的自适应多先验 Lasso
Incorporation of external information into high-dimensional modeling for gene expression data has been shown, both theoretically and empirically, to substantially enhance performance. Such external information, sometimes referred to as prior information or priors, has become increasingly accessible from multiple sources, yet its reliability may vary considerably. Existing approaches often integrate these priors without sufficiently accounting for their quality, which may…