Small area estimation methods under cut-off sampling
Isabel Molina and
No 2019-01, LISER Working Paper Series from LISER
Cut-off sampling is applied when there is a subset of units from the population from which getting the required information is too expensive or difficult and, therefore, those units are deliberately excluded from sample selection. If those excluded units are different from the sampled ones in the characteristics of interest, naïve estimators obtained by ignoring the cut-off sampling may be severely biased. Calibration estimators have been proposed to reduce the mentioned design-bias. However, the resulting estimators may have large variance when estimating in small domains. Similarly as calibration, model-based small area estimation methods using auxiliary information might decrease this bias if the assumed model holds for the whole population. At the same time, these methods provide more efficient estimators than calibration methods for small domains. We analyze the properties of calibration and model-based procedures for estimation of small domain characteristics under cut-off sampling. Our results confirm that the model-based estimators reduce the bias due to cut-off sampling and perform significantly better in terms of mean squared error.
Keywords: Calibration estimators; Cut-off sampling; EBLUP; EBP; Nested-error modelors; Unit level models (search for similar items in EconPapers)
Pages: 36 pages
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:irs:cepswp:2019-01
Access Statistics for this paper
More papers in LISER Working Paper Series from LISER 11, Porte des Sciences, L-4366 Esch-sur-Alzette, G.-D. Luxembourg. Contact information at EDIRC.
Bibliographic data for series maintained by Library and Documentation ().