{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T07:22:02Z","timestamp":1771658522160,"version":"3.50.1"},"reference-count":53,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2020,11,23]],"date-time":"2020-11-23T00:00:00Z","timestamp":1606089600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,2,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Data linkage can be used to combine values of the variable of interest from a national survey with values of auxiliary variables obtained from another source, such as a population register, for use in small area estimation. However, linkage errors can induce bias when fitting regression models; moreover, they can create non-representative outliers in the linked data in addition to the presence of potential representative outliers. In this paper, we adopt a secondary analyst\u2019s point of view, assuming that limited information is available on the linkage process, and develop small area estimators based on linear mixed models and M-quantile models to accommodate linked data containing a mix of both types of outliers. We illustrate the properties of these small area estimators, as well as estimators of their mean squared error, by means of model-based and design-based simulation experiments. We further illustrate the proposed methodology by applying it to linked data from the European Survey on Income and Living Conditions and the Italian integrated archive of economic and demographic micro data in order to obtain estimates of the average equivalised income for labour market areas in central Italy.<\/jats:p>","DOI":"10.1111\/rssb.12401","type":"journal-article","created":{"date-parts":[[2020,11,24]],"date-time":"2020-11-24T05:54:57Z","timestamp":1606197297000},"page":"78-107","source":"Crossref","is-referenced-by-count":10,"title":["Small Area Estimation with Linked Data"],"prefix":"10.1093","volume":"83","author":[{"given":"N.","family":"Salvati","sequence":"first","affiliation":[{"name":"Dipartimento di Economia e Management \u00a0 , Pisa , Italy"},{"name":"Universit\u00e0 di Pisa \u00a0 , Pisa , Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"E.","family":"Fabrizi","sequence":"additional","affiliation":[{"name":"Dipartimento di Scienze Economiche e Sociali \u00a0 , Milan , Italy"},{"name":"Universit\u00e0 Cattolica del Sacro Cuore \u00a0 , Milan , Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M. G.","family":"Ranalli","sequence":"additional","affiliation":[{"name":"Dipartimento di Scienze Politiche \u00a0 , Perugia , Italy"},{"name":"Universit\u00e0 degli Studi di Perugia \u00a0 , Perugia , Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"R. L.","family":"Chambers","sequence":"additional","affiliation":[{"name":"National Institute for Applied Statistics Research Australia \u00a0 \u00a0 , Wollongong , Australia"},{"name":"School of Mathematics and Applied Statistics \u00a0 \u00a0 , Wollongong , Australia"},{"name":"University of Wollongong \u00a0 \u00a0 , Wollongong , Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2020,11,23]]},"reference":[{"key":"2023022408370119100_","first-page":"170","volume-title":"Methodological developments in data linkage","author":"Abbott","year":"2016"},{"key":"2023022408370119100_","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1080\/01621459.1988.10478561","article-title":"An error component model for prediction of county crop areas using survey and satellite data","volume":"83","author":"Battese","year":"1988","journal-title":"Journal of the American Statistical 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