Robust skew-t factor analysis models for handling missing data

Wan-Lun Wang, Min Liu and Tsung-I Lin ()
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Wan-Lun Wang: Feng Chia University
Min Liu: University of Hawaii at Mānoa
Tsung-I Lin: National Chung Hsing University

Statistical Methods & Applications, 2017, vol. 26, issue 4, No 7, 649-672

Abstract: 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.

Keywords: Asymmetry; ECM algorithm; Imputation; Incomplete data; rMST distribution; STFA model; 62H12; 62H25 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s10260-017-0388-9

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