Autori
Wan-Lun, Wang
Min, Liu
Tsung-I, Lin

Titolo
Robust skew-t factor analysis models for handling missing data
Periodico
Statistical methods & applications : Journal of the Italian Statistical Society
Anno: 2017 - Volume: 26 - Fascicolo: 4 - Pagina iniziale: 649 - Pagina finale: 672

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.



SICI: 1618-2510(2017)26:4<649:RSFAMF>2.0.ZU;2-D

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