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Latent Variable Modeling of Diagnostic Accuracy
Biometrics, 1997Latent class analysis has been applied in medical research to assessing the sensitivity and specificity of diagnostic tests/diagnosticians. In these applications, a dichotomous latent variable corresponding to the unobserved true disease status of the patients is assumed.
Yang, Ilsoon, Becker, Mark P.
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Dimension in latent variable models
Journal of Mathematical Psychology, 2003zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Latent Variable Models with Fixed Effects
Biometrics, 1996We discuss latent variable models that allow for fixed effect covariates, as well as covariates affecting the latent variable directly. Restricted maximum likelihood and maximum likelihood are used to estimate model parameters. A generalized likelihood ratio test can be used to test significance of the covariates effecting the latent outcomes.
Sammel, Mary Dupuis, Ryan, Louise M.
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Latent variable models are network models
Behavioral and Brain Sciences, 2010AbstractCramer et al. present an original and interesting network perspective on comorbidity and contrast this perspective with a more traditional interpretation of comorbidity in terms of latent variable theory. My commentary focuses on the relationship between the two perspectives; that is, it aims to qualify the presumed contrast between ...
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Unidimensional linear latent variable models
1993Abstract: "Linear structural equation models with latent (unmeasured) variables are used widely in sociology, psychometrics, and political science. When such models have a unidimensional (pure) measurement model (Gerbing and Anderson 82, 88; Scheines 92) they imply constraints on the measured covariances which can be used to either confirm ...
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Statistical Methods in Medical Research, 2007
Anders, Skrondal, Sophia, Rabe-Hesketh
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Anders, Skrondal, Sophia, Rabe-Hesketh
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Latent Variable Interaction Modeling
Structural Equation Modeling: A Multidisciplinary Journal, 2002Latent variable interaction modeling with continuous observed variables is presented using 2 different approaches. The 1st approach analyzes data using a LISREL 8.30 program where the latent interaction variable is defined by multiplying pairs of observed variables.
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