Bayesian Scalar-on-Tensor Quantile Regression for Longitudinal Data on Alzheimer's Disease
阿尔茨海默病纵向数据的贝叶斯张量分位数回归
As a general and robust alternative to traditional mean regression models, quantile regression avoids the assumption of normally distributed errors, making it a versatile choice when modeling outcomes such as cognitive scores that typically have skewed distributions. Motivated by an application to Alzheimer's disease data where the aim is to explore how brain-behavior associations change over time, we propose a novel Bayesian tensor quantile regression for high-dimensional…