A flexible factor analysis based on the class of mean-mixture of normal distributions

Farzane Hashemi, Mehrdad Naderi, Ahad Jamalizadeh and Andriette Bekker

Computational Statistics & Data Analysis, 2021, vol. 157, issue C

Abstract: Factor analysis is a statistical technique for data reduction and structure detection that traditionally relies on the normality assumption for factors. However, due to the presence of non-normal features such as asymmetry and heavy tails in many practical situations, the first two moments cannot adequately explain the factors. An extension of the factor analysis model is introduced by assuming a generalization of the multivariate restricted skew-normal distribution for the vector of unobserved factors. An efficient and computationally tractable EM-type algorithm is adopted for computing the maximum likelihood estimates by presenting a hierarchical representation of the proposed model. Finally, the efficiency and advantages of the proposed novel methodology are demonstrated through both simulated and real benchmark datasets.

Keywords: Mean-mixture of normal distribution; EM-type algorithm; Factor analysis; Skewness and kurtosis (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S016794732030253X
Full text for ScienceDirect subscribers only.

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:eee:csdana:v:157:y:2021:i:c:s016794732030253x

DOI: 10.1016/j.csda.2020.107162

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().