Abstract
Abstract The present article examines the robustness of sampling strategies, that is, estimators and sample designs, from the point of view of attaining the minimum value of the Godambe—Joshi lower bound to the expected variance of a strategy. It is shown that asymptotic design unbiasedness is necessary but insufficient for robustness defined in the above sense. The concepts of weak and strong robustness of estimators, for given sampling strategies, are introduced. The article establishes that a sufficient condition for a sampling strategy to be robust is that the estimator is model unbiased and weakly robust. Sufficient conditions are also given for an estimator to be weakly robust. These conditions are then used to examine the robustness of strategies when the design matrix or the covariance matrix of the superpopulation model is misspecified. The robustness of model-based predictors is also examined.
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