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Activity Number:
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192
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Type:
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Topic Contributed
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Date/Time:
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Monday, July 30, 2007 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistics in Epidemiology
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| Abstract - #310305 |
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Title:
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Computationally Efficient Estimation of Multilevel High-Dimensional Latent Variable Models
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Author(s):
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Bengt Muthen*+ and Tihomir Asparouhov
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Companies:
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University of California, Los Angeles and Muthen & Muthen
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Address:
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3463 Stoner Ave, Los Angeles, CA, 90066,
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Keywords:
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Categorical variables ; Weighted least squares ; Twin analysis
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Abstract:
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Multilevel analysis often leads to modeling with multiple latent variables on several levels. While this is less of a problem with Gaussian observed variables, maximum-likelihood (ML) estimation with categorical outcomes presents computational problems due to multi-dimensional numerical integration. We describe a new method that compared to ML is both computationally efficient and has similar MSE. The method is an extension of the Muthen (1984) weighted least squares (WLS) estimation method for multilevel multivariate latent variable models for any combination of categorical, censored, and normal observed variables. Using a new version of the Mplus program, we compare MSE and the computational time for the ML and WLS estimators in a simulation study and present a longitudinal example where heritability is estimated using MZ and DZ twins.
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