Abstract
Consider the linear model
where β is an unknown parameter vector and the \(\{\varepsilon _{i}\}\) are i.i.d. errors. It is well known that ordinary least squares (LS) estimators are unbiased and consistent, but are not efficient when errors are heteroscedastic, and the usual standard error estimators of LS estimators are biased. Hence the usual confidence intervals and test statistics are biased and may lead to incorrect conclusions.
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Liang, H. (2014). Nonparametric and Semiparametric Regression for Independent Data. In: Davidian, M., Lin, X., Morris, J., Stefanski, L. (eds) The Work of Raymond J. Carroll. Springer, Cham. https://doi.org/10.1007/978-3-319-05801-6_4
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