Using prior-data conflict to tune Bayesian regularized regression models.

• Tác giả: Timofei Biziaev, Thierry Chekouo, Karen Kopciuk
• Ngôn ngữ: eng
• Ký hiệu phân loại: 579.165 Harmful organisms
• Thông tin xuất bản: United States : Statistics and computing , 2025
• Bộ sưu tập: NCBI
• ID: 675719

Tóm tắt

UNLABELLED: In high-dimensional regression models, variable selection becomes challenging from a computational and theoretical perspective. Bayesian regularized regression via shrinkage priors like the Laplace or spike-and-slab prior are effective methods for variable selection in SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11222-025-10582-1.