Trendafilov, Nickolay T.
|DOI (Digital Object Identifier) Link:||http://doi.org/10.1348/000711005X47168|
|Google Scholar:||Look up in Google Scholar|
The well-known problem of fitting the exploratory factor analysis model is reconsidered where the usual least squares goodness-of-fit function is replaced by a more resistant discrepancy measure, based on a smooth approximation of the ℓ1 norm. Fitting the factor analysis model to the sample correlation matrix is a complex matrix optimization problem which requires the structure preservation of the unknown parameters (e.g. positive definiteness). The projected gradient approach is a natural way of solving such data matching problems as especially designed to follow the geometry of the model parameters. Two reparameterizations of the factor analysis model are considered. The approach leads to globally convergent procedures for simultaneous estimation of the factor analysis matrix parameters. Numerical examples illustrate the algorithms and factor analysis solutions.
|Item Type:||Journal Article|
|Copyright Holders:||2005 The British Psychological Society|
|Academic Unit/Department:||Mathematics, Computing and Technology > Mathematics and Statistics
Mathematics, Computing and Technology
|Depositing User:||Sarah Frain|
|Date Deposited:||17 Aug 2010 13:40|
|Last Modified:||15 Jan 2016 14:03|
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