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• W1482777201 abstract "Often environmental epidemiological studies focuson estimating eects of highly correlated exposures on a healthcondition measured with multiple outcomes. Adjusting for allexposures as separate covariates in a multiple linear regressionmodel can cause multicollinearity. Furthermore, tting separatemodels for each exposure and outcome combination leads to problemsof multiple comparisons and may introduce confounding with theexposures left out of the model. Structural equation modeling(SEM) alleviates these issues by assuming a latent variablestructure underlying the observed exposures and outcomes. Bayesianmethods for estimating the parameters in SEM treat the latentvariables as missing data and impute them as part of a MarkovChain Monte Carlo (MCMC) sampler, resulting in the full posteriordistribution for both the parameters and the latent variables.Bayesian SEM is reviewed and illustrated with a model analyzingthe eects of phthalate exposure on human semen quality. Althoughmethods exist for checking overall goodness-of-t in SEM, littleattention has been given to testing specic model assumptions.Individual-level residuals are easy to estimate in the BayesianSEM and are used to dene posterior predictive checks for modelassumptions. The empirical cumulative distribution function of theresiduals is used to test the assumption that the residual erroris normally distributed. Cumulative sums of the residuals are usedto check the assumption that the predictors have a linearrelationship to the dependent variables in the model equations.The validity of the posterior predictive checks is examinedthrough simulation studies, and the tests are applied to theexample data set." @default.
• W1482777201 created "2016-06-24" @default.
• W1482777201 creator A5072529001 @default.
• W1482777201 date "2010-01-01" @default.
• W1482777201 modified "2023-09-24" @default.
• W1482777201 title "Residual diagnostic methods for Bayesian structural equation models" @default.
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• W1482777201 hasPublicationYear "2010" @default.
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