Automated learning of t factor analysis models with complete and incomplete data

Wan-Lun Wang, Luis M. Castro and Tsung-I Lin

Journal of Multivariate Analysis, 2017, vol. 161, issue C, 157-171

Abstract: The t factor analysis (tFA) model is a promising tool for robust reduction of high-dimensional data in the presence of heavy-tailed noises. When determining the number of factors of the tFA model, a two-stage procedure is commonly performed in which parameter estimation is carried out for a number of candidate models, and then the best model is chosen according to certain penalized likelihood indices such as the Bayesian information criterion. However, the computational burden of such a procedure could be extremely high to achieve the optimal performance, particularly for extensively large data sets. In this paper, we develop a novel automated learning method in which parameter estimation and model selection are seamlessly integrated into a one-stage algorithm. This new scheme is called the automated tFA (AtFA) algorithm, and it is also workable when values are missing. In addition, we derive the Fisher information matrix to approximate the asymptotic covariance matrix associated with the ML estimators of tFA models. Experiments on real and simulated data sets reveal that the AtFA algorithm not only provides identical fitting results, as compared to traditional two-stage procedures, but also runs much faster, especially when values are missing.

Keywords: Missing information; ML estimation; Model selection; Multivariate t distribution; Outliers (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0047259X17301409
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:jmvana:v:161:y:2017:i:c:p:157-171

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

DOI: 10.1016/j.jmva.2017.07.009

Access Statistics for this article

Journal of Multivariate Analysis is currently edited by de Leeuw, J.

More articles in Journal of Multivariate Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().