Design-based random permutation models with auxiliary information
Department of Medicine, Division of Preventive and Behavioral Medicine
We extend the random permutation model to obtain the best linear unbiased estimator of a finite population mean accounting for auxiliary variables under simple random sampling without replacement (SRS) or stratified SRS. The proposed method provides a systematic design-based justification for well-known results involving common estimators derived under minimal assumptions that do not require specification of a functional relationship between the response and the auxiliary variables.
auxiliary variable, design-based inference, prediction, finite sampling, random permutation model, simultaneous permutation