Functional Concurrent Regression Mixture Models Using Spiked Ewens-Pitman Attraction Priors.

ABSTRACT: Functional concurrent, or varying-coefficient, regression models are a form of functional data analysis methods in which functional covariates and outcomes are collected concurrently. Two active areas of research for this class of models are identifying influential functional covariates and clustering their relations across observations. In various applications, researchers have applied and developed methods to address these objectives separately. However, no approach currently performs both tasks simultaneously. In this paper, we propose a fully Bayesian functional concurrent regression mixture model that simultaneously performs functional variable selection and clustering for subject-specific trajectories. Our approach introduces a novel spiked Ewens-Pitman attraction prior that identifi

SUBMITTER: Liang M 

PROVIDER: S-EPMC11507269 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature