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Activity Number:
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579
- Sampling, Variance Estimation, and Advancements with Auxiliary Data
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Type:
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Contributed
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Date/Time:
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Wednesday, July 31, 2019 : 2:00 PM to 3:50 PM
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Sponsor:
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Survey Research Methods Section
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Abstract #305122
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Title:
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TRUMP: Tuned Regression Unbiased Mean Predictor
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Author(s):
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Sarjinder Singh* and Stephen Sedory
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Companies:
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Texas A&M University-Kingsville and Texas A & M University-Kingsville
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Keywords:
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Calibration;
TRUMP Cuts;
TRUMP Care Coefficient;
Chain Type TRUMP Cuts;
First Basic Information (FBI)
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Abstract:
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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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Authors who are presenting talks have a * after their name.
