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多层主效应矩阵因子模型AI 解读

We propose a multilevel main effects matrix factor model (MMEFM) to extract meaningful row and column effects from groups of matrix data. As a generalization of a multilevel matrix factor model, we provide rigorous definitions of MMEFM and identifications, together with iterative algorithms for the estimators of all global and local components in the main effects and common component, including global/local main effects and core ranks, with rates of convergence spelt out. An…

高维矩阵时间序列主效应因子模型的识别与稀疏性AI 解读

We propose a general identification framework for main effect factor models for matrix-valued time series. The classical sum-to-zero restriction on the row and column main effects is replaced by a broad class of shift-varying functions, which includes weighted averages, quantiles, and reference-unit anchors as special cases. We prove that the shift-varying property is necessary and sufficient for parameter identification, and we derive closed-form estimators under minimal…