A robust factor analysis model based on the canonical fundamental skew-t distribution

Tsung-I Lin, I-An Chen and Wan-Lun Wang ()
Additional contact information
Tsung-I Lin: National Chung Hsing University
I-An Chen: National Chung Hsing University
Wan-Lun Wang: National Cheng Kung University

Statistical Papers, 2023, vol. 64, issue 2, No 1, 367-393

Abstract: Abstract The traditional factor analysis rested on the assumption of multivariate normality has been extended by considering the restricted multivariate skew-t (rMST) distribution for the unobserved factors and errors jointly. However, the rMST distribution has limited use for characterising skewness that concentrates in a single direction. This paper is devoted to introducing a more flexible robust factor analysis model 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 toward 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, we present an information-based approach to approximating the asymptotic covariance matrix of the ML estimators. The effectiveness and applicability of the proposed techniques are demonstrated through the analysis of simulated and real datasets.

Keywords: AECM algorithm; Canonical fundamental skew-t distribution; Factor scores; Truncated multivariate t distribution; Unrestricted multivariate skew-t distribution (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00362-022-01318-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:stpapr:v:64:y:2023:i:2:d:10.1007_s00362-022-01318-8

Ordering information: This journal article can be ordered from
http://www.springer. ... business/journal/362

DOI: 10.1007/s00362-022-01318-8

Access Statistics for this article

Statistical Papers is currently edited by C. Müller, W. Krämer and W.G. Müller

More articles in Statistical Papers from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().