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Abstract Details
Activity Number:
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586
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Type:
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Contributed
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Date/Time:
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Wednesday, August 3, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistics and Marketing
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Abstract - #301641 |
Title:
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Order Detection for Factor Analysis Model Using Adaptive Group LASSO
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Author(s):
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Liuxia Wang*+ and Yulin Li
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Companies:
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Sentrana Inc. and K&L Consulting Services Inc
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Address:
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1725 I St. NW, Washington, DC, 20006,
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Keywords:
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Adaptive group lasso ;
selection consistency ;
factor analysis model ;
ECM algorithm
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Abstract:
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One crucial issue in factor analysis (FA) model is to detect the number of factors to summarize the original data. A wealth of approaches have been proposed for this matter. In this paper, we formulate the order detection for FA model into grouped variables selection in a regression, and the adaptive group lasso method is adopted for grouped variable selection. We show theoretically that the new method is able to identify the true number of factors consistently. We also illustrate this model using real and simulated examples. The simulations demonstrate that our approach outperforms some existing methods in terms of the percentage of correct order being detected.
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