Bayesian Generalized Low Rank Regression Models for Neuroimaging Phenotypes and Genetic Markers
神经影像表型与遗传标记的贝叶斯广义低秩回归
We propose a Bayesian generalized low rank regression model (GLRR) for the analysis of both high-dimensional responses and covariates. This development is motivated by performing searches for associations between genetic variants and brain imaging phenotypes. GLRR integrates a low rank matrix to approximate the high-dimensional regression coefficient matrix of GLRR and a dynamic factor model to model the high-dimensional covariance matrix of brain imaging phenotypes. Local…