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Robust bilinear factor analysis based on the matrix-variate $t$ distribution
This introduces two challenges for $t$fa: (i) the inherent matrix structure of the data is broken, and (ii) robustness may be lost, as vectorized matrix data typically results in a high data dimension, which could easily lead to the breakdown of $t$fa.
Choosing the number of factors in factor analysis with incomplete data via a hierarchical Bayesian information criterion
The Bayesian information criterion (BIC), defined as the observed data log likelihood minus a penalty term based on the sample size $N$, is a popular model selection criterion for factor analysis with complete data.
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