  Factor analysis is a classical data reduction technique that seeks a
potentially lower number of unobserved variables that can account for the
correlations among the observed variables. This paper presents an extension of
the factor analysis model by assuming jointly a restricted version of
multivariate skew t distribution for the latent factors and unobservable
errors, called the skew-t factor analysis model. The proposed model shows
robustness to violations of normality assumptions of the underlying latent
factors and provides flexibility in capturing extra skewness as well as heavier
tails of the observed data. A computationally feasible ECM algorithm is
developed for computing maximum likelihood estimates of the parameters. The
usefulness of the proposed methodology is illustrated by a real-life example
and results also demonstrates its better performance over various existing
methods.
