Robust skew-t factor analysis models for handling missing data

Wan Lun Wang, Min Liu, Tsung I. Lin

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)649-672
Number of pages24
JournalStatistical Methods and Applications
Issue number4
Publication statusPublished - 2017 Nov 1

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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