Mean Mixture Normal Factor Analysis for Handling Missing Data

This paper presents an extension of the factor analysis model based on the mean mixture normal in the presence of nonresponses and missing data. Missing data may occur due to operator error or incomplete data capturing therefore they cannot be ignored in factor analysis modeling. We implement an EM-type algorithm for maximum likelihood estimation and propose single imputation of possible missing values under a missing at random mechanism.