An Unbiased Regression Type Estimator In Randomized Response Sampling

Roberto Arias, Stephen A. Sedory and Sarjinder Singh ()
Additional contact information
Roberto Arias: Texas A&M University-Kingsville
Stephen A. Sedory: Texas A&M University-Kingsville
Sarjinder Singh: Texas A&M University-Kingsville

Sankhya B: The Indian Journal of Statistics, 2022, vol. 84, issue 1, No 9, 243-258

Abstract: Abstract In this paper, we suggest a new method of constructing an unbiased regression type estimator in randomized response sampling. We introduce two new randomized response estimators, one we created through the utilization of a sum of special products technique and the other through the utilization of the method used for computing a matrix determinant. This new idea of making an unbiased regression type estimator proves to be more efficient with no loss in respondent protection. Analytical comparisons show the proposed unbiased regression type estimator is always more efficient than the considered competitors. The theoretical justification that the proposed estimator has a smaller variance over its competitors is crystal clear, so no simulation study is required. However to study the gain in magnitude of the relative efficiency, a simulation study has been carried out.

Keywords: Randomized response; sensitive characteristic; relative protection and efficiency.; Primary 62D05; Secondary 62D99 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s13571-021-00256-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sankhb:v:84:y:2022:i:1:d:10.1007_s13571-021-00256-z

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/13571

DOI: 10.1007/s13571-021-00256-z

Access Statistics for this article

Sankhya B: The Indian Journal of Statistics is currently edited by Dipak Dey

More articles in Sankhya B: The Indian Journal of Statistics from Springer, Indian Statistical Institute
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().