Rotation to Sparse Loadings Using [Formula: see text] Losses and Related Inference Problems.

ABSTRACT: Researchers have widely used exploratory factor analysis (EFA) to learn the latent structure underlying multivariate data. Rotation and regularised estimation are two classes of methods in EFA that they often use to find interpretable loading matrices. In this paper, we propose a new family of oblique rotations based on component-wise [Formula: see text] loss functions [Formula: see text] that is closely related to an [Formula: see text] regularised estimator. We develop model selection and post-selection inference procedures based on the proposed rotation method. When the true loading matrix is sparse, the proposed method tends to outperform traditional rotation and regularised estimation methods in terms of statistical accuracy and computational cost. Since the proposed loss functions ar

SUBMITTER: Liu X 

PROVIDER: S-EPMC10188427 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature