Factor Augmented Matrix Regression
因子增广矩阵回归
We introduce Factor-Augmented Matrix Regression (FAMAR) to address the growing applications of matrix-variate data and their associated challenges, particularly with high-dimensionality and covariate correlations. FAMAR encompasses two key algorithms. The first is a novel non-iterative approach that efficiently estimates the factors and loadings of the matrix factor model, using techniques of pre-training, diverse projection, and block-wise averaging. The second algorithm…