Covariate Measurement Error in Quadratic Regression
We consider quadratic regression models where the explanatory variable is measured with error. The effect of classical measurement error is to flatten the curvature of the estimated function. The effect on the observed turning point depends on the location of the true turning point relative to the population mean of the true predictor. Two methods for adjusting parameter estimates for the measurement error are compared. First, two versions of regression calibration estimation are considered. The second approach uses moment-based methods which require no assumptions about the distribution of the covariates measured with error.
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