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Bayesian Variable Selection for High-Dimensional Predictors with Missing Psychometric Outcomes
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 Bayesia
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