Generalized Matrix Factor Model

TL;DR

This paper proposes a nonlinear generalized matrix factor model (GMFM) that handles mixed-type variables, with theoretical guarantees, model selection, and algorithms, demonstrating superior performance in simulations and real data analysis.

Contribution

Introduces GMFM extending linear models to mixed data types, with novel estimation methods, theoretical properties, and practical algorithms.

Findings

GMFM outperforms existing models in handling discrete and mixed data.

Theoretical convergence rates for estimated parameters are established.

Algorithms effectively compute the maximum likelihood estimates.

Abstract

This article introduces a nonlinear generalized matrix factor model (GMFM) that allows for mixed-type variables, extending the scope of linear matrix factor models (LMFM) that are so far limited to handling continuous variables. We introduce a novel augmented Lagrange multiplier method, equivalent to the constraint maximum likelihood estimation, and carefully tailored to be locally concave around the true factor and loading parameters. This statistically guarantees the local convexity of the negative Hessian matrix around the true parameters of the factors and loadings, which is nontrivial in the matrix factor modeling and leads to feasible central limit theorems of the estimated factors and loadings. We also theoretically establish the convergence rates of the estimated factor and loading matrices for the GMFM under general conditions that allow for correlations across samples, rows, and columns. Moreover, we provide a model selection criterion to determine the numbers of row and column factors consistently. To numerically compute the constraint maximum likelihood estimator, we provide two algorithms: two-stage alternating maximization and minorization maximization. Extensive simulation studies demonstrate GMFM's superiority in handling discrete and mixed-type variables. An empirical data analysis of the company's operating performance shows that GMFM does clustering and reconstruction well in the presence of discontinuous entries in the data matrix.