Calibration Estimation in Survey Sampling
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Jae Kwang | - |
dc.contributor.author | Park, Mingue | - |
dc.date.accessioned | 2021-09-08T04:08:14Z | - |
dc.date.available | 2021-09-08T04:08:14Z | - |
dc.date.created | 2021-06-11 | - |
dc.date.issued | 2010-04 | - |
dc.identifier.issn | 0306-7734 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/116700 | - |
dc.description.abstract | Calibration estimation, where the sampling weights are adjusted to make certain estimators match known population totals, is commonly used in survey sampling. The generalized regression estimator is an example of a calibration estimator. Given the functional form of the calibration adjustment term, we establish the asymptotic equivalence between the functional-form calibration estimator and an instrumental variable calibration estimator where the instrumental variable is directly determined from the functional form in the calibration equation. Variance estimation based on linearization is discussed and applied to some recently proposed calibration estimators. The results are extended to the estimator that is a solution to the calibrated estimating equation. Results from a limited simulation study are presented. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | WILEY | - |
dc.subject | EMPIRICAL LIKELIHOOD | - |
dc.subject | AUXILIARY INFORMATION | - |
dc.subject | MODEL | - |
dc.subject | PREDICTION | - |
dc.title | Calibration Estimation in Survey Sampling | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Park, Mingue | - |
dc.identifier.doi | 10.1111/j.1751-5823.2010.00099.x | - |
dc.identifier.scopusid | 2-s2.0-77955785587 | - |
dc.identifier.wosid | 000276246500003 | - |
dc.identifier.bibliographicCitation | INTERNATIONAL STATISTICAL REVIEW, v.78, no.1, pp.21 - 39 | - |
dc.relation.isPartOf | INTERNATIONAL STATISTICAL REVIEW | - |
dc.citation.title | INTERNATIONAL STATISTICAL REVIEW | - |
dc.citation.volume | 78 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 21 | - |
dc.citation.endPage | 39 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Statistics & Probability | - |
dc.subject.keywordPlus | EMPIRICAL LIKELIHOOD | - |
dc.subject.keywordPlus | AUXILIARY INFORMATION | - |
dc.subject.keywordPlus | MODEL | - |
dc.subject.keywordPlus | PREDICTION | - |
dc.subject.keywordAuthor | Benchmarking estimator | - |
dc.subject.keywordAuthor | domain estimation | - |
dc.subject.keywordAuthor | generalized regression estimator | - |
dc.subject.keywordAuthor | instrumental variable regression estimator | - |
dc.subject.keywordAuthor | variance estimation | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.