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  3. Mathematik, Informatik und Statistik - Open Access LMU - Teil 01/03 Podcast
  4. Estimating A Polynomial Regression With Measurement Errors In The Structural And In The Functional Case - A Comparison
Two methods of estimating the parameters of a polynomial regression
with measurement errors in the regressor variable are compared to
each other with respect to their relative efficiency and
robustness. One of the two estimators (SLS) is valid for the
structural variant of the model and uses the assumption that the
true regressor variable is normally distributed, while the other
one (ALS and also its small sample modification MALS) does not need
any assumption on the regressor distribution. SLS turns out to
react rather strongly on violations of the normality assumption as
far as its bias is concerned but is quite robust with respect to
its MSE. It is more efficient than ALS or MALS whenever the
normality assumption holds true.

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