A robust factor analysis model using the restricted skew-t distribution

• Tsung-I Lin ^[1] ; Pal H. Wu ^[1] ; Geoffrey J. McLachlan ^[2] ; Sharon X. Lee ^[2]
  1. [1] National Chung Hsing University

     National Chung Hsing University

     Taiwán

     
  2. [2] University of Queensland

     University of Queensland

     Australia

     
• Localización: Test: An Official Journal of the Spanish Society of Statistics and Operations Research, ISSN-e 1863-8260, ISSN 1133-0686, Vol. 24, Nº. 3, 2015, págs. 510-531
• Idioma: inglés
• DOI: 10.1007/s11749-014-0422-2
• Texto completo no disponible (Saber más ...)
• Resumen

  • Factor analysis is a classical data-reduction technique that seeks a potentially lower number of unobserved variables that can account for the correlations among the observed variables. This paper presents an extension of the factor analysis model, called the skew-t factor analysis model, constructed by assuming a restricted version of the multivariate skew-t distribution for the latent factors and a symmetric t-distribution for the unobservable errors jointly. The proposed model shows robustness to violations of normality assumptions of the underlying latent factors and provides flexibility in capturing extra skewness as well as heavier tails of the observed data. A computationally feasible expectation conditional maximization algorithm is developed for computing maximum likelihood estimates of model parameters. The usefulness of the proposed methodology is illustrated using both simulated and real data.