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Zig-zag exploratory factor analysis with more variables than observations

Unkel, Steffen and Trendafilov, Nickolay T. (2013). Zig-zag exploratory factor analysis with more variables than observations. Computational Statistics, 28(1) pp. 107–125.

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DOI (Digital Object Identifier) Link: https://doi.org/10.1007/s00180-011-0275-z
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Abstract

In this paper, the problem of fitting the exploratory factor analysis (EFA) model to data matrices with more variables than observations is reconsidered. A new algorithm named ‘zig-zag EFA’ is introduced for the simultaneous least squares estimation of all EFA model unknowns. As in principal component analysis, zig-zag EFA is based on the singular value decomposition of data matrices. Another advantage of the proposed computational routine is that it facilitates the estimation of both common and unique factor scores. Applications to both real and artificial data illustrate
the algorithm and the EFA solutions.

Item Type: Journal Item
Copyright Holders: 2011 Springer-Verlag
ISSN: 1613-9658
Keywords: factor analysis; horizontal data matrices; procrustes problems; singular value decomposition; gene expression data; Thurstone’s box data
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM)
Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics
Item ID: 35886
Depositing User: Nickolay Trendafilov
Date Deposited: 19 Dec 2012 16:52
Last Modified: 07 Dec 2018 13:25
URI: http://oro.open.ac.uk/id/eprint/35886
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