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Sarjinder Singh

Texas A&M University at Kingsville



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Stephen A. Sedory

Texas A&M University-Kingsville



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301 – Weighting Adjustments

Trump: Tuned Ratio Unbiased Mean Predictor

Sponsor: Survey Research Methods Section
Keywords: Calibration, unbiasedness, model assistance, performance, simulation study

Sarjinder Singh

Texas A&M University at Kingsville

Stephen A. Sedory

Texas A&M University-Kingsville

In this paper, we show that the new concept for tuning design weights in survey sampling developed by Singh, Sedory, Rueda, Arcos and Arnab (2015: Elsevier) leads to an innovative Tuned Ratio Unbiased Mean Predictor (TRUMP) with the assistance of a model. It is shown theoretically that the proposed TRUMP is more efficient than the ratio estimator due to Cochran (1940). Although there is no need of empirical investigations, but a small scale simulation study will be discussed. The proposed TRUMP has potential to be extended to the regression predictor and other complex survey sampling designs.

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