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Stephen A. Sedory

Texas A&M University-Kingsville



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Sarjinder Singh

Texas A&M University-Kingsville



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579 – Sampling, Variance Estimation, and Advancements with Auxiliary Data

TRUMP: Tuned Regression Unbiased Mean Predictor

Sponsor: Survey Research Methods Section
Keywords: Calibration, TRUMP Cuts, TRUMP Care Coefficient, Chain Type TRUMP Cuts, First Basic Information (FBI)

Stephen A. Sedory

Texas A&M University-Kingsville

Sarjinder Singh

Texas A&M University-Kingsville

In this paper, we introduce a new Tuned Regression Unbiased Mean Predictor (TRUMP) which we show that can be adjusted for smaller variance than the linear regression predictor due to Hansen, Hurwitz and Madow (1953) when there is Heteroscedasticity, which we call here Hillary Campaign Coefficient(H). Thus the proposed new TRUMP model can be made more efficient than the Best Linear Unbiased Predictor (BLUP) based on the choice of a TRUMP Care coefficient (g). R-codes to find values of the TRUMP Care Coefficient (g) for beating the Hillary Campaign Coefficient(H) are also included. At the end, use of multi-auxiliary variables case has been discussed.

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