title: Design-based random permutation models with auxiliary information¶
abstract: 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.
authors: University of Massachusetts Medical School, Worcester, MA, USA.
authors: Wenjun
authors: W
authors: Li
authors: Edward J
authors: EJ
authors: Stanek
authors: Julio M
authors: JM
authors: Singer
keywords: auxiliary variable; design-based inference; finite sampling; prediction; random permutation model; simultaneous permutation
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