579 – Sampling, Variance Estimation, and Advancements with Auxiliary Data
TRUMP: Tuned Regression Unbiased Mean Predictor
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.