A0184
Title: A robust factor analysis model utilizing the canonical fundamental skew-t distribution
Authors: Wan-Lun Wang - National Cheng Kung University (Taiwan)
Tsung-I Lin - National Chung Hsing University (Taiwan) [presenting]
I-An Chen - National Chung Hsing University (Taiwan)
Abstract: The traditional factor analysis, which relied on the assumption of multivariate normality, has been extended by jointly incorporating the restricted multivariate skew-t (rMST) distribution for the unobserved factors and errors. However, the limited utility of the rMST distribution in capturing skewness concentrated in a single direction prompted the development of a more adaptable and robust factor analysis model. A more flexible, robust factor analysis model is introduced based on the broader canonical fundamental skew-t (CFUST) distribution, called the CFUSTFA model. The proposed new model can account for more complex features of skewness in multiple directions. An efficient alternating expectation conditional maximization algorithm fabricated under several reduced complete-data spaces is developed to estimate parameters under the maximum likelihood (ML) perspective. To assess the variability of parameter estimates, an information-based approach is presented to approximate the asymptotic covariance matrix of the ML estimators. The efficacy and practicality of the proposed techniques are demonstrated through the analysis of simulated and real datasets.
