{"id":"https://openalex.org/W2166127828","doi":null,"title":"A prediction approach to sampling design","display_name":"A prediction approach to sampling design","publication_year":2005,"publication_date":"2005-12-01","ids":{"openalex":"https://openalex.org/W2166127828","mag":"2166127828"},"language":"en","primary_location":{"id":"pmh:oai:RePEc:ssb:dispap:440","doi":null,"is_oa":false,"landing_page_url":"https://www.ssb.no/a/publikasjoner/pdf/DP/dp440.pdf","pdf_url":"https://www.ssb.no/a/publikasjoner/pdf/DP/dp440.pdf","source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"preprint"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://hdl.handle.net/10419/192422","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100720313","display_name":"Li\u2010Chun Zhang","orcid":"https://orcid.org/0000-0002-3944-9484","observed_orcids":["https://orcid.org/0000-0002-3944-9484"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li\u2010Chun Zhang","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-3944-9484","affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5111412819","display_name":"Ib Thomsen","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ib Thomsen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":7,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13030","display_name":"Survey Sampling and Estimation Techniques","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T13030","display_name":"Survey Sampling and Estimation Techniques","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11798","display_name":"Optimal Experimental Design Methods","score":0.00019999999494757503,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11539","display_name":"Survey Methodology and Nonresponse","score":0.0,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/model-based-prediction","display_name":"model-based prediction","score":0.7450000047683716},{"id":"https://openalex.org/keywords/sampling-design","display_name":"sampling design","score":0.8669999837875366},{"id":"https://openalex.org/keywords/personalized-prediction","display_name":"personalized prediction","score":0.8899999856948853},{"id":"https://openalex.org/keywords/probability-sampling","display_name":"probability sampling","score":0.7390000224113464},{"id":"https://openalex.org/keywords/unequal-probability-sampling","display_name":"unequal probability sampling","score":0.8389999866485596},{"id":"https://openalex.org/keywords/two-stage-sampling","display_name":"two-stage sampling","score":0.9520000219345093},{"id":"https://openalex.org/keywords/stratified-sampling","display_name":"stratified sampling","score":0.8069999814033508},{"id":"https://openalex.org/keywords/proportional-allocation","display_name":"proportional allocation","score":0.9649999737739563},{"id":"https://openalex.org/keywords/simple-random-sampling","display_name":"simple random sampling","score":0.7889999747276306},{"id":"https://openalex.org/keywords/business-surveys","display_name":"business surveys","score":0.6330000162124634}],"concepts":[{"id":"https://openalex.org/C75373757","wikidata":"https://www.wikidata.org/wiki/Q7410160","display_name":"Sampling design","level":3,"score":0.7564612627029419},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.6810644268989563},{"id":"https://openalex.org/C82152865","wikidata":"https://www.wikidata.org/wiki/Q7208505","display_name":"Poisson sampling","level":5,"score":0.6384884119033813},{"id":"https://openalex.org/C49898467","wikidata":"https://www.wikidata.org/wiki/Q1517706","display_name":"Stratified sampling","level":2,"score":0.5836036205291748},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.5452213883399963},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5183402299880981},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.5018415451049805},{"id":"https://openalex.org/C20353970","wikidata":"https://www.wikidata.org/wiki/Q1056998","display_name":"Simple random sample","level":3,"score":0.4958411157131195},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.4879107177257538},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.46647176146507263},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4574829936027527},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.4330231249332428},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.40327513217926025},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3848392367362976},{"id":"https://openalex.org/C170593435","wikidata":"https://www.wikidata.org/wiki/Q4128565","display_name":"Slice sampling","level":4,"score":0.32363975048065186},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.17507824301719666},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.10765093564987183},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"pmh:oai:RePEc:ssb:dispap:440","doi":null,"is_oa":false,"landing_page_url":"https://www.ssb.no/a/publikasjoner/pdf/DP/dp440.pdf","pdf_url":"https://www.ssb.no/a/publikasjoner/pdf/DP/dp440.pdf","source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"preprint"},{"id":"pmh:oai:econstor.eu:10419/192422","doi":null,"is_oa":true,"landing_page_url":"http://hdl.handle.net/10419/192422","pdf_url":"http://hdl.handle.net/10419/192422","source":{"id":"https://openalex.org/S4306401696","display_name":"Econstor (Econstor)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"doc-type:workingPaper"},{"id":"mag:2166127828","doi":null,"is_oa":false,"landing_page_url":"http://www.stat.jyu.fi/sae2005/abstracts/zhang.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"RePEc: Research Papers in Economics","raw_type":null},{"id":"pmh:oai:ssb.brage.unit.no:11250/180243","doi":null,"is_oa":true,"landing_page_url":"http://hdl.handle.net/11250/180243","pdf_url":null,"source":{"id":"https://openalex.org/S7407064446","display_name":"NORA - Norwegian Open Research Archives","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"27 s.","raw_type":"info:eu-repo/semantics/workingPaper"}],"best_oa_location":{"id":"pmh:oai:econstor.eu:10419/192422","doi":null,"is_oa":true,"landing_page_url":"http://hdl.handle.net/10419/192422","pdf_url":"http://hdl.handle.net/10419/192422","source":{"id":"https://openalex.org/S4306401696","display_name":"Econstor (Econstor)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"doc-type:workingPaper"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2166127828.pdf","grobid_xml":"https://content.openalex.org/works/W2166127828.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2094655094","https://openalex.org/W2397710007","https://openalex.org/W2086802324","https://openalex.org/W8142894","https://openalex.org/W7159817090","https://openalex.org/W3025239458","https://openalex.org/W4298020000","https://openalex.org/W2902694913","https://openalex.org/W1968659147","https://openalex.org/W7214173784"],"abstract_inverted_index":{"Abstract:\\nStandard":[0],"approaches":[1],"to":[2,58,65,93,98,134,149,215],"sample":[3],"surveys":[4],"take":[5],"as":[6],"the":[7,11,42,55,59,70,72,79,88,95,100,109,122,140,160,164,177,183,189,194],"point":[8,224],"of":[9,13,44,61,87,121,152,166,179,196,203,219,225],"departure":[10],"estimation":[12,36],"one":[14],"or\\nseveral":[15],"population":[16,123],"totals":[17,66],"(or":[18,25],"means),":[19],"or":[20],"a":[21,130,150,180,222],"few":[22],"predefined":[23],"sub-totals":[24],"sub-means).":[26],"While":[27],"the\\nmodel-based":[28],"prediction":[29,60,74,102,120,223],"approach":[30,57,133,212],"provides":[31,129],"an":[32,217],"attractive":[33],"framework":[34],"for":[35,41,78,111],"and":[37,67,125,172,186],"inference,":[38],"a\\nmodel-based":[39],"theory":[40],"variety":[43],"randomization":[45],"sampling":[46,96,135,143,161,169,175],"designs":[47],"has":[48],"been":[49],"lacking.":[50],"In":[51],"this":[52],"paper\\nwe":[53],"extend":[54],"model-based":[56,132,208],"individuals":[62],"in":[63,144,159],"addition":[64],"means.\\nSince,":[68],"given":[69],"sample,":[71,89],"conditional":[73],"error":[75],"is":[76,91],"zero":[77],"selected":[80],"units":[81,85],"but":[82],"positive":[83],"for\\nthe":[84],"outside":[86],"it":[90,146],"possible":[92],"use":[94,165,195],"design":[97,154],"control":[99,126],"unconditional\\nindividual":[101],"mean":[103],"square":[104],"errors.":[105],"This":[106],"immediately":[107],"raises":[108],"need":[110],"probability":[112,142,174,232],"sampling.":[113],"It\\nturns":[114],"out":[115],"that":[116,156],"balancing":[117],"between":[118],"optimal":[119],"total":[124],"over":[127],"individual\\npredictions":[128],"fruitful":[131],"design.":[136],"Apart":[137],"from":[138,221],"raising":[139],"need\\nfor":[141],"general,":[145],"leads":[147],"naturally":[148],"number":[151],"important":[153],"features":[155],"are\\nfirmly":[157],"established":[158],"practice,":[162],"including":[163],"simple":[167],"random":[168],"for\\nhomogeneous":[170],"populations":[171],"unequal":[173,231],"otherwise,":[176],"division":[178],"business\\npopulation":[181],"into":[182],"take-all,":[184],"take-some":[185],"take-none":[187],"units,":[188],"most":[190],"common":[191,238],"two-stage":[192,234],"sampling\\ndesigns,":[193],"stratification":[197],"with":[198],"proportional":[199],"allocation,":[200],"etc..":[201],"Most":[202],"them":[204],"have":[205],"not":[206],"received\\nadequate":[207],"treatment":[209],"previously.":[210],"Our":[211],"enables":[213],"us":[214],"give":[216],"appraisal":[218],"these\\nmethods":[220],"view.\\nKeywords:":[226],"Individual":[227],"prediction,":[228],"business":[229],"survey,":[230],"sampling,":[233],"sampling,\\nlinear":[235],"regression":[236],"population,":[237],"parameter":[239],"model":[240]},"counts_by_year":[],"updated_date":"2026-09-03T10:48:47.769340","created_date":"2016-06-24T00:00:00"}