Bayesian longitudinal low-rank regression models for imaging genetic data from longitudinal studies
Introduction
L2R2 is a function for estimating L2R2 model with MCMC algorithm. This L2R2 package is developed by Zhao-Hua Lu, Zakaria Khondker, and Hongtu Zhu from the BIG-S2 lab. To perform a joint analysis of multivariate neuroimaging phenotypes and candidate genetic markers obtained from longitudinal studies, we develop a Bayesian longitudinal low-rank regression (L2R2) model. The L2R2 model integrates three key methodologies: a low-rank matrix for approximating the high-dimensional regression coefficient matrices corresponding to the genetic main effects and their interactions with time, penalized splines for characterizing the overall time effect, and a sparse factor analysis model coupled with random effects for capturing within-subject spatio-temporal correlations of longitudinal phenotypes. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm.
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References
1. Hongtu Zhu, Zakaria Khondker, Zhaohua Lu and Joseph G. Ibrahim. Bayesian Generalized Low Rank Regression Models for Neuroimaging Phenotypes and Genetic Markers. Journal of the American Statistical Association. 2014; 109 (507) 977-990.