Bayesian Skewed-t Multivariate Censored Quantile Regression for Functional Neuroimaging Data
Abstract
This paper proposes a Bayesian skewed-t multivariate censored quantile regression model tailored for functional neuroimaging data, such as EEG or fMRI signals. The model accommodates functional covariates, multivariate right-censored responses, and asymmetric heavy-tailed error distributions. Functional principal component analysis (FPCA) is employed to reduce dimensionality in the functional inputs, while posterior inference is carried out using Hamiltonian Monte Carlo (HMC). Simulation studies demonstrate the superior performance of the proposed method compared to classical alternatives. Application to real neuroimaging data confirms its robustness and effectiveness in capturing heterogeneous quantile-dependent effects across multiple outcomes.
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