Probability-Sampling Approach to Editing
AbstractEditing for measurement errors is always part of data processing. In traditional editing, all data records are checked for errors and inconsistencies. In a new way of editing, only the subset with the most important erroneous responses is considered for editing. This approach is applied in selective editing procedures, which have been shown to save resources considerably. However, selective editing lacks a probabilistic basis and the properties of estimators cannot be established using standard methods. In particular,
bias properties of the estimator are unknown except for level estimates based on historical data. This paper proposes combining selective editing with an editing procedure based on the traditional probability-sampling framework. The variance of a bias-corrected Horvitz-Thompson estimator is derived and a variance estimator is proposed. The results of a simulation study support the use of the combined editing procedure.
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