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A novel robust meta-analysis model using the $t$ distribution for outlier accommodation and detection
Although robust modeling using the $t$ distribution is an appealing idea, the existing work, that explores the use of the $t$ distribution only for random effects, involves complicated numerical integration and numerical optimization.
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.
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