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The asymptotic distribution of a class of L-estimators under long range dependence

Research output: Contribution to journalJournal article


<mark>Journal publication date</mark>06/1999
<mark>Journal</mark>Canadian Journal of Statistics
Issue number2
Number of pages16
Pages (from-to)345-360
<mark>Original language</mark>English


This 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.