{"data":{"id":"10.6084/m9.figshare.c.2115059","type":"dois","attributes":{"doi":"10.6084/m9.figshare.c.2115059","prefix":"10.6084","suffix":"m9.figshare.c.2115059","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Sarjinder Singh","affiliation":[],"nameIdentifiers":[]},{"name":"Sedory, Stephen Andrew","nameType":"Personal","givenName":"Stephen Andrew","familyName":"Sedory","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Two-step calibration of design weights in survey sampling"}],"publisher":"Figshare","container":{},"publicationYear":2015,"subjects":[{"subject":"Biotechnology"},{"subject":"59999 Environmental Sciences not elsewhere classified","subjectScheme":"FOR"},{"subject":"FOS: Earth and related environmental sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Earth and related environmental sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Immunology"},{"subject":"FOS: Clinical medicine","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Clinical medicine","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"69999 Biological Sciences not elsewhere classified","subjectScheme":"FOR"},{"subject":"FOS: Biological sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Biological sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"80699 Information Systems not elsewhere classified","subjectScheme":"FOR"},{"subject":"FOS: Computer and information sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Computer and information sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"19999 Mathematical Sciences not elsewhere classified","subjectScheme":"FOR"},{"subject":"FOS: Mathematics","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Mathematics","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Cancer"},{"subject":"Science Policy"},{"subject":"Mental Health"},{"subject":"Plant Biology"}],"contributors":[],"dates":[{"date":"2015-12-05","dateType":"Created"},{"date":"2016-05-04","dateType":"Updated"},{"date":"2015","dateType":"Issued"}],"language":null,"types":{"ris":"GEN","bibtex":"misc","citeproc":"article","schemaOrg":"Collection","resourceType":"Collection","resourceTypeGeneral":"Collection"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rights":"CC-BY","rightsUri":"http://creativecommons.org/licenses/by/3.0/us"}],"descriptions":[{"description":"In this article, a new two-step calibration technique of design weights is proposed. In the first step, the calibration weights are set proportional to the design weights in a given sample. In the second step, the constants of proportionality are determined based on different objectives of the investigator such as bias reduction or minimum mean squared error. Many estimators available in the literature can be shown to be special cases of the proposed two-step calibrated estimator. A simulation study, based on a real data set, is also included at the end. A few technical issues are raised with respect to the use of the proposed calibration technique: both limitations and benefits are discussed.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"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