Subspace decompositions for association structure learning in multivariate categorical response regression

Autor:	Zhao, Hongru, Molstad, Aaron J., Rothman, Adam J.
Rok vydání:	2024
Předmět:	Statistics - Methodology Mathematics - Statistics Theory 62H20 62J12(Primary) 62F10 62H12 (Secondary)
Druh dokumentu:	Working Paper
Popis:	Modeling the complex relationships between multiple categorical response variables as a function of predictors is a fundamental task in the analysis of categorical data. However, existing methods can be difficult to interpret and may lack flexibility. To address these challenges, we introduce a penalized likelihood method for multivariate categorical response regression that relies on a novel subspace decomposition to parameterize interpretable association structures. Our approach models the relationships between categorical responses by identifying mutual, joint, and conditionally independent associations, which yields a linear problem within a tensor product space. We establish theoretical guarantees for our estimator, including error bounds in high-dimensional settings, and demonstrate the method's interpretability and prediction accuracy through comprehensive simulation studies. Comment: 31 pages, 2 figures, 8 sections, journal paper
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2410.04356 Zobrazit plný text záznamu View this record from Arxiv