Abstract
This paper presents a robust extension of factor analysis model by assuming the multivariate normal mean–variance mixture of Birnbaum–Saunders distribution for the unobservable factors and errors. A computationally analytical EM-based algorithm is developed to find maximum likelihood estimates of the parameters. The asymptotic standard errors of parameter estimates are derived under an information-based paradigm. Numerical merits of the proposed methodology are illustrated using both simulated and real datasets.
Acknowledgments
We are very grateful to the chief editor, associate editor and reviewers for their valuable comments and insightful suggestions that greatly improved this paper.
Disclosure statement
No potential conflict of interest was reported by the authors.