  Factor analysis is a flexible technique for assessment of multivariate
dependence and codependence. Besides being an exploratory tool used to reduce
the dimensionality of multivariate data, it allows estimation of common factors
that often have an interesting theoretical interpretation in real problems.
However, in some specific cases the interest involves the effects of latent
factors not only in the mean, but in the entire response distribution,
represented by a quantile. This paper introduces a new class of models, named
quantile factor models, which combines factor model theory with
distribution-free quantile regression producing a robust statistical method.
Bayesian estimation for the proposed model is performed using an efficient
Markov chain Monte Carlo algorithm. The proposed model is evaluated using
synthetic datasets in different settings, in order to evaluate its robustness
and performance under different quantiles compared to more usual methods. The
model is also applied to a financial sector dataset and a heart disease
experiment.
