Robust skew-<Emphasis Type="Italic">t</Emphasis> factor analysis models for handling missing data |
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Authors: | Wan-Lun Wang Min Liu Tsung-I Lin |
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Institution: | 1.Department of Statistics, Graduate Institute of Statistics and Actuarial Science,Feng Chia University,Taichung,Taiwan;2.Department of Educational Psychology,University of Hawaii at Mānoa,Honolulu,USA;3.Institute of Statistics,National Chung Hsing University,Taichung,Taiwan;4.Department of Public Health,China Medical University,Taichung,Taiwan |
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Abstract: | This paper presents a novel framework 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 the multivariate skew-t distribution for the latent factors and the unobservable errors to accommodate non-normal features such as asymmetry and heavy tails or outliers. An EM-type algorithm is developed to carry out ML estimation and imputation of missing values under a missing at random mechanism. The practical utility of the proposed methodology is illustrated through real and synthetic data examples. |
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