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A0450
Title: Bayesian semiparametric measurement error models with application to dose response analysis Authors:  Taeryon Choi - Korea University (Korea, South) [presenting]
Abstract: The aim is to consider Bayesian semiparametric measurement error models, or errors-in-variable regression models based on Fourier series and Dirichlet process mixture, in which the true covariate is not observable, but the surrogate of the true covariate, is only observed. The proposed methodology is compared with other existing approaches to Bayesian measurement error models in simulation studies and bench mark data example. More importantly, we consider a real data application for meta analysis with dose-response analysis, in which measurement errors and shape constraints in the regression functions need to be incorporated with inter-study variability.