Population empirical likelihood estimation in dual frame surveys

Maria Mar Rueda (), Maria Giovanna Ranalli (), Antonio Arcos () and David Molina ()
Additional contact information
Maria Mar Rueda: University of Granada
Maria Giovanna Ranalli: University of Perugia
Antonio Arcos: University of Granada
David Molina: University of Granada

Statistical Papers, 2021, vol. 62, issue 5, No 19, 2473-2490

Abstract: Abstract Dual frame surveys are a device to reduce the costs derived from data collection in surveys and improve coverage for the whole target population. Since their introduction, in the 1960‘s, dual frame surveys have gained much attention and several estimators have been formulated based on a number of different approaches. In this work, we propose new dual frame estimators based on the population empirical likelihood method originally proposed by Chen and Kim (Stat Sin 24:335–355, 2014) and using both the dual and the single frame approach. The extension of the proposed methodology to more than two frame surveys is also sketched. The performance of the proposed estimators in terms of relative bias and relative mean squared error is tested through simulation experiments. These experiments indicate that the proposed estimators yield better results than other likelihood-based estimators proposed in the literature.

Keywords: Multiplicity; Auxiliary information; Multiple frame surveys; Finite population inference; 62D05 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://link.springer.com/10.1007/s00362-020-01200-5 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:stpapr:v:62:y:2021:i:5:d:10.1007_s00362-020-01200-5

Ordering information: This journal article can be ordered from
http://www.springer. ... business/journal/362

DOI: 10.1007/s00362-020-01200-5

Access Statistics for this article

Statistical Papers is currently edited by C. Müller, W. Krämer and W.G. Müller

More articles in Statistical Papers from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().