Local influence in maximum likelihood factor analysis

Abstract

The influence of observations in maximum likelihood factor analysis is investigated by using the local influence method. The method of local influence allows simultaneous perturbations on all observations so that it can identify multiple influential observations. Under an appropriate perturbation we can get information about individually and jointly influential observations by studying the curvatures and the associated direction Vectors of the perturbation-formed surface of the maximum likelihood estimator for a parameter, based on the sample covariance or correlation matrix. An illustrative example is given to show the effectiveness of the local influence method on the identification of influential observations.