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Activity Number: 252
Type: Contributed
Date/Time: Monday, August 10, 2015 : 2:00 PM to 3:50 PM
Sponsor: Social Statistics Section
Abstract #316650 View Presentation
Title: Approximated Penalized Maximum Likelihood for Exploratory Factor Analysis
Author(s): Fan Wallentin* and Shaobo Jin and Irini Moustaki
Companies: Uppsala University and Uppsala University and London School of Economics
Keywords: Factor Rotation ; LASSO ; Sparsity ; Shrinkage

This study is concerned with approximated penalized maximum likelihood (APML) for exploratory factor analysis. A simulation study is conducted to investigate the performance of the APML estimator. The APML estimator was found to frequently contain the correct loading structure and improve the varimax rotation if the loading matrix is not perfect simple. With a proper choice of the penalty term and a tuning parameter selection method, APML generally produces a lower mean squared error for the factor loadings, the covariance (or correlation) matrix and the Thompson factor scores.

Authors who are presenting talks have a * after their name.

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