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Please use this identifier to cite or link to this item:
http://krishi.icar.gov.in/jspui/handle/123456789/8242
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hukum Chandraa,∗, , | en_US |
dc.contributor.author | Nicola Salvati b | en_US |
dc.contributor.author | Ray Chambersc | en_US |
dc.contributor.author | Nikos Tzavidis | en_US |
dc.date.accessioned | 2018-10-26T09:22:02Z | - |
dc.date.available | 2018-10-26T09:22:02Z | - |
dc.date.issued | 2012-02-15 | - |
dc.identifier.citation | Not Available | en_US |
dc.identifier.issn | Not Available | - |
dc.identifier.uri | http://krishi.icar.gov.in/jspui/handle/123456789/8242 | - |
dc.description | Not Available | en_US |
dc.description.abstract | A geographical weighted empirical best linear unbiased predictor (GWEBLUP) for a small area average is proposed, and an estimator of its conditional mean squared error is developed. The popular empirical best linear unbiased predictor under the linear mixed model is obtained as a special case of the GWEBLUP. Empirical results using both modelbased and design-based simulations, with the latter based on two real data sets, show that the GWEBLUP predictor can lead to efficiency gains when spatial nonstationarity is present in the data. A practical gain from using the GWEBLUP is in small area estimation for out of sample areas. In this case the efficient use of geographical information can potentially improve upon conventional synthetic estimation | en_US |
dc.description.sponsorship | Not Available | en_US |
dc.language.iso | English | en_US |
dc.publisher | Elsevier B.V. | en_US |
dc.relation.ispartofseries | Not Available; | - |
dc.subject | Borrowing strength over space | en_US |
dc.subject | Geographical weighted regression | en_US |
dc.subject | Out of sample small area estimation | en_US |
dc.subject | Spatial analysis | en_US |
dc.title | Small area estimation under spatial nonstationarity | en_US |
dc.title.alternative | Not Available | en_US |
dc.type | Research Paper | en_US |
dc.publication.projectcode | Not Available | en_US |
dc.publication.journalname | Computational Statistics and Data Analysis | en_US |
dc.publication.volumeno | Not Available | en_US |
dc.publication.pagenumber | Not Available | en_US |
dc.publication.divisionUnit | Not Available | en_US |
dc.publication.sourceUrl | doi:10.1016/j.csda.2012.02.006 | en_US |
dc.publication.authorAffiliation | ICAR::Indian Agricultural Statistics Research Institute | en_US |
dc.publication.authorAffiliation | Dipartimento di Statistica e Matematica Applicata all’Economia, University of Pisa, Italy | en_US |
dc.publication.authorAffiliation | Centre for Statistical and Survey Methodology, University of Wollongong, Wollongong, NSW, 2522, Australia | en_US |
dc.publication.authorAffiliation | Social Statistics and S3RI, University of Southampton, United Kingdom | en_US |
dc.ICARdataUseLicence | http://krishi.icar.gov.in/PDF/ICAR_Data_Use_Licence.pdf | en_US |
dc.publication.naasrating | 7.19 | - |
Appears in Collections: | AEdu-IASRI-Publication |
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