EconPapers    
Economics at your fingertips  
 

Estimation of finite population distribution function of sensitive variable*

Sanghamitra Pal and Purnima Shaw

Communications in Statistics - Theory and Methods, 2023, vol. 52, issue 4, 1318-1331

Abstract: The finite population proportion of a sensitive characteristic is estimated indirectly by using Randomized Response (RR) Techniques (RRT’s) pioneered by Warner (1965) followed by several other RRT’s in the literature. The existing literature contains several RRT’s for estimating the finite population mean of the sensitive quantitative variable. However, there might be a situation when the population proportion bearing the value of the stigmatizing variable below a threshold is of more concern than the exact population mean. The problem hence reduces to the estimation of the finite population distribution function of a quantitative sensitive variable. Following Chaudhuri and Saha (2004), a logistic regression approach has been used to estimate the finite population proportion bearing value of the stigmatizing variable below a threshold. As an alternative to this method, this article also attempts to provide suitable modifications for sensitive variables, in the estimation of distribution function proposed by Chaudhuri and Shaw (2020), when the variable of interest is innocuous. Numerical results based on a simulated population present interesting finding on the proposed methodologies.

Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2021.1934030 (text/html)
Access to full text is restricted to subscribers.

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:taf:lstaxx:v:52:y:2023:i:4:p:1318-1331

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20

DOI: 10.1080/03610926.2021.1934030

Access Statistics for this article

Communications in Statistics - Theory and Methods is currently edited by Debbie Iscoe

More articles in Communications in Statistics - Theory and Methods from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().

 
Page updated 2025-03-20
Handle: RePEc:taf:lstaxx:v:52:y:2023:i:4:p:1318-1331