Regression calibration with instrumental variables and non-parametric regression for longitudinal data
Regression usually assumes exactly known values for the covariates, with random error in the response only. In some situations the covariates themselves must be estimated using proxy variables and models of instrumental variables. The following study seeks to extend methods for estimating regression parameters and inferential statistics under conditions of longitudinal data when interactions between covariates are involved. Longitudinal data introduces random subject effects and correlated error terms into models for the covariate and the response. Interaction introduce second order terms ...
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