On joint identification and latent variable estimation in factor analysis models
Giorgio Picci, Stefano Pinzoni · 2002
A factor analysis model is a representation y=Ax+e, of m observable variables y=[y/sub 1/....y/sub 2/]/sup T/, assumed zero-mean and with finite variance, as linear combinations of n common factors x=[x/sub 1/...x/sub 2/]/sup T/, plus uncorrelated "noise" or "error" terms e=[e/sub 1/...e/sub 2/]/sup T/. It is imposed that the m components of the error e be mutually uncorrelated random variables. The aim of the model is provide an "explanation" of the mutual interrelation between the observable variables y in terms of small number of common factors.>