Sparse Bayesian Factor Models with Mass-Nonlocal Factor Scores
采用点质量与非局部因子得分先验的稀疏贝叶斯因子模型
Bayesian factor models are widely used for dimensionality reduction and pattern discovery in high-dimensional datasets across diverse fields. These models typically focus on imposing priors on factor loading to induce sparsity and improve interpretability. However, factor scores, which play a critical role in individual-level associations with factors, have received less attention and are assumed to follow a standard normal distribution. This assumption oversimplifies the…