Flexible Factor Model for Handling Missing Data in Supervised Learning

This paper presents an extension of the factor analysis model based on the normal mean-variance mixture of the Birnbaum-Saunders in the presence of nonresponses and missing data. This model can be used as a powerful tool to model non-normal features observed from data such as strongly skewed and heavy-tailed noises. Missing data may occur due to operator error or incomplete data capturing therefore 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. The potential and applicability of our proposed method are illustrated through analyzing both simulated and real datasets.