Abstract
Consider a partially linear regression model with an unknown vector parameter β, an unknown functiong(·), and unknown heteroscedastic error variances. In this paper we develop an asymptotic semiparametric generalized least squares estimation theory under some weak moment conditions. These moment conditions are satisfied by many of the error distributions encountered in practice, and our theory does not require the number of replications to go to infinity.
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Chen, G., You, J. An asymptotic theory for semiparametric generalized least squares estimation in partially linear regression models. Statistical Papers 46, 173–193 (2005). https://doi.org/10.1007/BF02762967
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DOI: https://doi.org/10.1007/BF02762967