EconPapers    
Economics at your fingertips  
 

Adjusted variance estimators based on minimizing mean squared error for stratified random samples

Guoyi Zhang and Bruce Swan

Statistical Theory and Related Fields, 2024, vol. 8, issue 2, 117-123

Abstract: In the realm of survey data analysis, encountering substantial variance relative to bias is a common occurrence. In this study, we present an innovative strategy to tackle this issue by introducing slightly biased variance estimators. These estimators incorporate a constant c within the range of 0 to 1, which is determined through the minimization of Mean Squared Error (MSE) for $ c \times (\hbox{variance estimator}) $ c×(variance estimator). This research builds upon the foundation laid by Kourouklis (2012, A new estimator of the variance based on minimizing mean squared error. The American Statistician, 66(4), 234–236) and extends their work into the domain of survey sampling. Extensive simulation studies are conducted to illustrate the superior performance of the adjusted variance estimators when compared to standard variance estimators, particularly in terms of MSE. These findings underscore the efficacy of our proposed approach in enhancing the precision of variance estimation within the context of survey data analysis.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/24754269.2024.2303915 (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:tstfxx:v:8:y:2024:i:2:p:117-123

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tstf20

DOI: 10.1080/24754269.2024.2303915

Access Statistics for this article

Statistical Theory and Related Fields is currently edited by Zhao Wei

More articles in Statistical Theory and Related Fields from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().

 
Page updated 2025-03-20
Handle: RePEc:taf:tstfxx:v:8:y:2024:i:2:p:117-123