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Activity Number:
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250
- Weighting and Variance Estimation in Complex Samples
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Type:
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Contributed
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Date/Time:
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Tuesday, August 9, 2022 : 8:30 AM to 10:20 AM
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Sponsor:
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Survey Research Methods Section
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Abstract #322835
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Title:
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Bias Interrupters Developed Estimators’ Network (BIDEN) with a Mixture of TRUMP Cuts and Jackknifing
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Author(s):
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Sarjinder Singh* and Stephen A. 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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Ratio estimator;
Bias;
Quenouille’s method;
Relative Efficiency;
TRUMP Cuts;
Jackknifing
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Abstract:
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Quenouille (1956) introduced the idea that Jackknifing can be used to reduce bias resulting from using the ratio estimator due to Cochran (1940). Singh and Sedory (2017a) proposed the Tuned Ratio Unbiased Mean Predictor (TRUMP) where they introduced the idea of TRUMP Cuts. In this paper, we introduce the Bias Interrupters Developed Estimators’ Network (BIDEN) which utilises the help of TRUMP Cuts and Jackknifing. We show that proper use of what we call BIDEN Care Coefficients could reduce the bias when using the ratio estimator even more than that obtained when using Quenouille’s method. It could also be made more efficient than the sample mean estimator with an appropriated choice of TRUMP Care Coefficient. These new findings are supported with exact numerical computations using a well known set of data available in Horvitz and Thompson (1952).
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Authors who are presenting talks have a * after their name.
