Abstract
AbstractThis paper obtains asymptotic representations of a class of L‐estimators in a linear regression model when the errors are a function of long‐range‐dependent Gaussian random variables. These representations are then used to address some of the efficiency robustness properties of L‐estimators compared to the least‐squares estimator. It is observed that under the Gaussian error distribution, each member of the class has the same asymptotic efficiency as that of the least‐squares estimator. The results are obtained as a consequence of the asymptotic uniform linearity of some weighted empirical processes based on long‐range‐dependent random variables.
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