Future of Tuned Ratio Unbiased Mean Predictor (TRUMP) with the Unified Scrambling Approach (USA)
Abstract Number:
2488
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Sarjinder Singh (1), Stephen Sedory (2)
Institutions:
(1) Texas A&M University-Kingsville, N/A, (2) Texas A & M University - Kingsville, N/A
Co-Author:
First Author:
Presenting Author:
Abstract Text:
The Tuned Ratio Unbiased Mean Predictor (TRUMP) was introduced by Singh and Sedory (2017: Survey Research Methods Section, Proceedings of the American Statistical Association, pp. 1746-1759). Based on First Basic Information (FBI) about the TRUMP care coefficient, they have shown that the proposed TRUMP can perform better than the Best Linear Unbiased Estimator (BLUE) and also can perform better than the Best Linear Unbiased Predictor (BLUP). Warner (1965: Journal of the American Statistical Association, pp. 63-69) introduced the idea of estimating the population proportion of the sensitive attributes by making use of a randomization device. Later on, the idea was extended to estimate the population mean of a sensitive variable by making use of additive and multiplicative scrambling approaches. In this paper, we will study the future of the TRUMP with the Unified Scrambling Approach (USA) on the lines of Singh, Joarder and King (1996: Australian Journal of Statistics, pp. 201-211). The results based on theory and simulation study will be reported.
Keywords:
Population Mean|Scrambled Responses|Jackknifing|TRUMP cuts| Linear model|
Sponsors:
Survey Research Methods Section
Tracks:
Privacy and Confidentiality Methods
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