Bayesian Variable Selection for High-Dimensional Predictors with Missing Psychometric Outcomes
心理测量结局缺失时高维预测变量的贝叶斯选择
High-dimensional, multimodal predictors and partially observed multivariate outcomes are common in psychometric research. However, existing regularization methods often do not accommodate hierarchical predictor structures and are primarily designed for univariate outcomes. We propose SHIM, a Bayesian framework for structured variable selection that combines hierarchical horseshoe shrinkage with a Bayesian treatment of missing outcomes. The framework jointly accommodates…