A Bures-Wasserstein Formulation of Matrix Decomposition Structural Equation Modeling

Abstract

Matrix decomposition SEM (MDSEM) is a data-matrix-based alternative to conventional covariance-based SEM, but its theoretical relationship to covariance-based SEM and the statistical properties of its estimator have remained unclear. We first reformulate MDSEM using a single loss function that integrates the measurement and structural models. We then prove that minimizing this loss over the model parameters is equivalent to minimizing the squared Bures-Wasserstein (BW) distance between the observed and model-implied covariance matrices, and use this equivalence to establish the estimator's theoretical properties. Specifically, the equivalence identifies the proposed estimator as both an MDSEM estimator and a covariance-based SEM estimator within the minimum discrepancy estimation framework, from which consistency, asymptotic normality, and standard errors are derived. Simulations show that its finite-sample performance is comparable to that of conventional estimators, including maximum likelihood, and that confidence-interval coverage is close to the nominal level. The estimator is also more numerically stable than conventional SEM estimators in small samples and under model misspecification. Thus, the BW formulation provides a theoretical foundation for MDSEM and a practically useful discrepancy function for covariance-based SEM.