Abstract
This paper proposes a class of adaptive estimators based on ranks for the parameter of a linear regression model with ARMA errors. Under local asymptotic normality, these estimators are shown to be locally asymptotically minimax. Adaptivity is obtained via two distinct approaches: the first one relies on a consistent estimator of the innovation density computed from the order statistic of residuals, and the second one on an estimator of the score function by adapting the kernel estimator method.
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