Estimation of the linear-plateau segmented regression model in the presence of measurement error |
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Authors: | Scott D. Grimshaw |
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Affiliation: | College of Business and Management , University of Maryland , College Park, MD, 20742 |
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Abstract: | It is well known that when the true values of the independent variable are unobservable due to measurement error, the least squares estimator for a regression model is biased and inconsistent. When repeated observations on each xi are taken, consistent estimators for the linear-plateau model can be formed. The repeated observations are required to classify each observation to the appropriate line segment. Two cases of repeated observations are treated in detail. First, when a single value of yi is observed with the repeated observations of xi the least squares estimator using the mean of the repeated xi observations is consistent and asymptotically normal. Second, when repeated observations on the pair (xi, yi ) are taken the least squares estimator is inconsistent, but two consistent estimators are proposed: one that consistently estimates the bias of the least squares estimator and adjusts accordingly; the second is the least squares estimator using the mean of the repeated observations on each pair. |
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Keywords: | Errors-in-variables classification repeated observations spline regression |
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