Exploratory factor analysis-Parameter estimation and scores prediction with high-dimensional data

In an approach aiming at high-dimensional situations, we first introduce a distribution-free approach to parameter estimation in the standard random factor model, that is shown to lead to the same estimating equations as maximum likelihood estimation under normality. The derivation is considerably simpler, and works equally well in the case of more variables than observations (p>n). We next concentrate on the latter case and show results of type: