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Abstract Details
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Activity Number:
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524
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Type:
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Contributed
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Date/Time:
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Wednesday, August 3, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistics in Epidemiology
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| Abstract - #303399 |
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Title:
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Generalized Multiple Indicators, Multiple Causes Measurement Error Models
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Author(s):
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Carmen Dwele Tekwe*+ and Randy L. Carter
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Companies:
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Texas A & M University and University at Buffalo
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Address:
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Department of Statistics, College Station, TX, 77840,
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Keywords:
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Berkson error ;
measurement error ;
MIMIC models ;
non-linear models ;
structural equation models ;
atomic bomb survivors
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Abstract:
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Multiple indicators, multiple causes (MIMIC) models are useful for studying the effects of a latent variable on several outcomes, when causes of the explanatory latent variable are observed. Classical measurement error is uncorrelated with the latent variable; while a Berkson error is uncorrelated with its estimate. Previous work has focused on MIMIC models, where the causes of the latent variable are observed without error. We generalize the MIMIC model to allow non-linear relationships and also allow both Berkson error and classical measurement error in the determinants of the causal equation by defining the G-MIMIC ME model. We propose MC-EM based estimation procedures for the G-MIMIC ME models and apply our results to data collected on atomic bomb survivors.
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