Consistency of the Bayesian Information Criterion for Model Selection in Exploratory Factor Analysis
Hien Duy Nguyen, Kei Hirose

TL;DR
This paper proves that the Bayesian information criterion (BIC) consistently identifies the correct factor model order in exploratory factor analysis, even under model misspecification, by analyzing covariance structures directly.
Contribution
It establishes the strong consistency of BIC for model selection in factor analysis under broad conditions, including misspecification and various covariance structures.
Findings
BIC is strongly consistent for pseudo-true factor order.
The proof works directly in covariance space without regularization.
The results extend to other information criteria with similar penalties.
Abstract
We study model selection by the Bayesian information criterion (BIC) in fixed-dimensional exploratory factor analysis over a fixed finite family of compact covariance classes. Our main result shows that the BIC is strongly consistent for the pseudo-true factor order under misspecification, provided that all globally optimal models share a common pseudo-true covariance set, the population Gaussian criterion has a local quadratic margin away from that set, and the BIC complexity counts are order-separating at the pseudo-true order. The candidate models may have an unknown mean vector, exact-zero restrictions in the loading matrix, and either diagonal or spherical error covariance structures, and the selection target is the smallest candidate factor order that yields the best Gaussian approximation, in Kullback--Leibler divergence, to the data-generating covariance structure. The proof…
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