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Mixture models in measurement error problems, with reference to epidemiological studies

Listed author(s):
  • Sylvia Richardson
  • Laurent Leblond
  • Isabelle Jaussent
  • Peter J. Green
Registered author(s):

    The paper focuses on a Bayesian treatment of measurement error problems and on the question of the specification of the prior distribution of the unknown covariates. It presents a flexible semiparametric model for this distribution based on a mixture of normal distributions with an unknown number of components. Implementation of this prior model as part of a full Bayesian analysis of measurement error problems is described in classical set-ups that are encountered in epidemiological studies: logistic regression between unknown covariates and outcome, with a normal or log-normal error model and a validation group. The feasibility of this combined model is tested and its performance is demonstrated in a simulation study that includes an assessment of the influence of misspecification of the prior distribution of the unknown covariates and a comparison with the semiparametric maximum likelihood method of Roeder, Carroll and Lindsay. Finally, the methodology is illustrated on a data set on coronary heart disease and cholesterol levels in blood. Copyright 2002 Royal Statistical Society.

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    Article provided by Royal Statistical Society in its journal Journal of the Royal Statistical Society: Series A (Statistics in Society).

    Volume (Year): 165 (2002)
    Issue (Month): 3 ()
    Pages: 549-566

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    Handle: RePEc:bla:jorssa:v:165:y:2002:i:3:p:549-566
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