B0563
Title: Skew-t factor analysis models with incomplete data
Authors: Tsung-I Lin - National Chung Hsing University (Taiwan) [presenting]
Abstract: A novel framework is presented for maximum likelihood (ML) estimation in skew-t factor analysis (STFA) models in the presence of missing values (or nonresponses). As a robust extension of the ordinary factor analysis model, the STFA model assumes a restricted version of multivariate skew-$t$ distribution for the latent factors and unobservable errors to accommodate non-normal features such as asymmetry and heavy tails or outliers. A computationally analytical EM-type algorithm is developed to carry out ML estimation and imputation of missing values under the missing at random mechanism. The practical utility of the proposed methodology is illustrated through both real and synthetic data examples.
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