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Activity Number:
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24
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Type:
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Contributed
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Date/Time:
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Sunday, August 2, 2009 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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| Abstract - #305861 |
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Title:
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Exploratory Longitudinal Factor Analysis
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Author(s):
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Sherry Lin*+
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Companies:
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University of California, Los Angeles
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Address:
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, , ,
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Keywords:
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Longitudinal data ; exploratory factor analysis ; cholesky decomposition ; hierarchical priors
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Abstract:
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Factor analysis is a technique to model observed variables as linear combinations of a fewer number of unobserved factors. Longitudinal data introduces additional complexity to the FA model by the need to account for correlation of variables across time. We present a method to fit exploratory longitudinal FA models via the Cholesky decomposition of the covariance matrix inverse. A Bayesian hierarchical prior allows for parsimonious estimation of the covariance matrix by identifying potential zeros in the Cholesky factor. The results may be used as input to existing confirmatory procedures, such as structural equation modeling, to gain additional information. We provide an example using medical data to illustrate the method.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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