Doubly structured sparsity for grouped multivariate responses with application to functional outcome score modeling.

ABSTRACT: This work is motivated by the need to accurately model a vector of responses related to pediatric functional status using administrative health data from inpatient rehabilitation visits. The components of the responses have known and structured interrelationships. To make use of these relationships in modeling, we develop a two-pronged regularization approach to borrow information across the responses. The first component of our approach encourages joint selection of the effects of each variable across possibly overlapping groups of related responses and the second component encourages shrinkage of effects towards each other for related responses. As the responses in our motivating study are not normally-distributed, our approach does not rely on an assumption of multivariate normality of

SUBMITTER: Huling JD 

PROVIDER: S-EPMC10535367 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature