{"id":"https://openalex.org/W7224076966","doi":"https://doi.org/10.1177/18747655261501357","title":"One weight for many variables: why multipurpose calibration of non-probability samples needs flexible working models, and what sharing it costs","display_name":"One weight for many variables: why multipurpose calibration of non-probability samples needs flexible working models, and what sharing it costs","publication_year":2026,"publication_date":"2026-10-28","ids":{"openalex":"https://openalex.org/W7224076966","doi":"https://doi.org/10.1177/18747655261501357"},"language":"en","primary_location":{"id":"doi:10.1177/18747655261501357","doi":"https://doi.org/10.1177/18747655261501357","is_oa":false,"landing_page_url":"https://doi.org/10.1177/18747655261501357","pdf_url":null,"source":{"id":"https://openalex.org/S2764680059","display_name":"Statistical Journal of the IAOS","issn_l":"1874-7655","issn":["1874-7655","1875-9254"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["abdc-c","anvur-scientific","cwts-core","jufo-1","norway-1","poland-40","russia-white-list-3","vabb-shw"],"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Statistical Journal of the IAOS","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137429745","display_name":"Juan Pablo Ferreira-Neira","orcid":"https://orcid.org/0000-0002-1884-8187","observed_orcids":["https://orcid.org/0000-0002-1884-8187"]},"institutions":[{"id":"https://openalex.org/I180910786","display_name":"Universidad de la Rep\u00fablica de Uruguay","ror":"https://ror.org/030bbe882","country_code":"UY","type":"education","lineage":["https://openalex.org/I180910786"]},{"id":"https://openalex.org/I4210133698","display_name":"National Institute of Statistics","ror":"https://ror.org/03ftrk873","country_code":"ES","type":"other","lineage":["https://openalex.org/I4210133698"]},{"id":"https://openalex.org/I4210139036","display_name":"Instituto Nacional de Estat\u00edstica","ror":"https://ror.org/04d9bp554","country_code":"CV","type":"education","lineage":["https://openalex.org/I4210139036"]}],"countries":["CV","ES","UY"],"is_corresponding":true,"raw_author_name":"Juan Pablo Ferreira","raw_affiliation_strings":["Facultad de Ciencias Econ\u00f3micas y de Administraci\u00f3n, Universidad de la Rep\u00fablica, Montevideo, Uruguay","Instituto Nacional de Estad\u00edstica (INE)"],"raw_orcid":"https://orcid.org/0000-0002-1884-8187","affiliations":[{"raw_affiliation_string":"Facultad de Ciencias Econ\u00f3micas y de Administraci\u00f3n, Universidad de la Rep\u00fablica, Montevideo, Uruguay","institution_ids":["https://openalex.org/I180910786"]},{"raw_affiliation_string":"Instituto Nacional de Estad\u00edstica (INE)","institution_ids":["https://openalex.org/I4210133698","https://openalex.org/I4210139036"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5137429745"],"corresponding_institution_ids":["https://openalex.org/I180910786","https://openalex.org/I4210133698","https://openalex.org/I4210139036"],"apc_list":{"value":3900,"currency":"USD","value_usd":3900},"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.83348037,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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":0.9359999895095825,"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":0.9359999895095825,"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/T11539","display_name":"Survey Methodology and Nonresponse","score":0.031199999153614044,"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"}},{"id":"https://openalex.org/T13546","display_name":"Census and Population Estimation","score":0.004999999888241291,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/non-probability-samples","display_name":"non-probability samples","score":0.9049999713897705},{"id":"https://openalex.org/keywords/model-calibration","display_name":"model calibration","score":0.6679999828338623},{"id":"https://openalex.org/keywords/doubly-robust-estimation","display_name":"doubly robust estimation","score":0.7519999742507935},{"id":"https://openalex.org/keywords/machine-learning","display_name":"machine learning","score":0.5320000052452087},{"id":"https://openalex.org/keywords/cross-fitting","display_name":"cross-fitting","score":0.6330000162124634},{"id":"https://openalex.org/keywords/rank-deficiency","display_name":"rank deficiency","score":0.6190000176429749},{"id":"https://openalex.org/keywords/standard-error-estimation","display_name":"standard error estimation","score":0.5889999866485596},{"id":"https://openalex.org/keywords/household-survey-microdata","display_name":"household survey microdata","score":0.7089999914169312}],"concepts":[{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.6638000011444092},{"id":"https://openalex.org/C2780551164","wikidata":"https://www.wikidata.org/wiki/Q2306599","display_name":"Column (typography)","level":3,"score":0.6265000104904175},{"id":"https://openalex.org/C2778355071","wikidata":"https://www.wikidata.org/wiki/Q1933849","display_name":"Microdata (statistics)","level":4,"score":0.5990999937057495},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.5220000147819519},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4747999906539917},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.46950000524520874},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.4652999937534332},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.4088999927043915},{"id":"https://openalex.org/C2781162219","wikidata":"https://www.wikidata.org/wiki/Q26250693","display_name":"Replicate","level":2,"score":0.4059000015258789},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.40470001101493835},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.36910000443458557},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3603000044822693},{"id":"https://openalex.org/C18747219","wikidata":"https://www.wikidata.org/wiki/Q620994","display_name":"Standard error","level":2,"score":0.3528999984264374},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.33239999413490295},{"id":"https://openalex.org/C48677424","wikidata":"https://www.wikidata.org/wiki/Q6888088","display_name":"Mode (computer interface)","level":2,"score":0.328900009393692},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.3082999885082245},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3066999912261963},{"id":"https://openalex.org/C22679943","wikidata":"https://www.wikidata.org/wiki/Q159375","display_name":"Standard deviation","level":2,"score":0.3046000003814697},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.2865000069141388},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.28200000524520874},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.274399995803833},{"id":"https://openalex.org/C175291020","wikidata":"https://www.wikidata.org/wiki/Q1156822","display_name":"Offset (computer science)","level":2,"score":0.26989999413490295},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.2612000107765198},{"id":"https://openalex.org/C2778348673","wikidata":"https://www.wikidata.org/wiki/Q739302","display_name":"Production (economics)","level":2,"score":0.2583000063896179}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1177/18747655261501357","doi":"https://doi.org/10.1177/18747655261501357","is_oa":false,"landing_page_url":"https://doi.org/10.1177/18747655261501357","pdf_url":null,"source":{"id":"https://openalex.org/S2764680059","display_name":"Statistical Journal of the IAOS","issn_l":"1874-7655","issn":["1874-7655","1875-9254"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["abdc-c","anvur-scientific","cwts-core","jufo-1","norway-1","poland-40","russia-white-list-3","vabb-shw"],"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Statistical Journal of the IAOS","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W314192993","https://openalex.org/W1591713837","https://openalex.org/W1918432791","https://openalex.org/W2019293164","https://openalex.org/W2029859258","https://openalex.org/W2077563060","https://openalex.org/W2078117364","https://openalex.org/W2100420630","https://openalex.org/W2127699081","https://openalex.org/W2164629226","https://openalex.org/W2612145410","https://openalex.org/W2612843304","https://openalex.org/W2883811944","https://openalex.org/W2902064890","https://openalex.org/W2921797130","https://openalex.org/W2980548856","https://openalex.org/W3000214403","https://openalex.org/W3008477519","https://openalex.org/W3016418892","https://openalex.org/W3017850173","https://openalex.org/W3046330016","https://openalex.org/W3091811668","https://openalex.org/W3106770771","https://openalex.org/W3123436326","https://openalex.org/W4283808675","https://openalex.org/W4312203614","https://openalex.org/W4399638520","https://openalex.org/W4399648906","https://openalex.org/W4405082968","https://openalex.org/W7136290524","https://openalex.org/W7148608107","https://openalex.org/W7163907785","https://openalex.org/W7204873887","https://openalex.org/W7211994010"],"related_works":["https://openalex.org/W2626726037","https://openalex.org/W7222914826","https://openalex.org/W2029859258","https://openalex.org/W4413771281","https://openalex.org/W2551225197","https://openalex.org/W2187047327","https://openalex.org/W6926362329","https://openalex.org/W4396798434","https://openalex.org/W2397054794","https://openalex.org/W1545007590"],"abstract_inverted_index":{"National":[0],"statistical":[1],"offices":[2],"apply":[3],"one":[4,30,107],"weight":[5],"column":[6,66],"to":[7,73,151,163],"every":[8,74],"variable,":[9],"while":[10],"the":[11,24,57,85,116,130,145,155,173,175,181,190,194],"doubly":[12],"robust":[13],"and":[14,52,71,76,136,153,189],"machine-learning":[15],"adjustments":[16],"that":[17,42,103],"non-probability":[18],"sources":[19],"require":[20],"are":[21,119],"outcome-specific;":[22],"reconciling":[23],"two":[25],"is":[26,53,63,133,178],"open.":[27],"We":[28],"construct":[29],"multipurpose":[31],"production":[32],"weight:":[33],"a":[34,39,64,96,122,134,137,158],"response-propensity":[35],"pseudo-weight":[36],"followed":[37],"by":[38,149,180,186],"model":[40],"calibration":[41,117],"stacks":[43],"K":[44,187],"study":[45],"variables,":[46],"each":[47],"with":[48,56,79],"its":[49,126],"own":[50],"learner,":[51],"exactly":[54],"consistent":[55],"known":[58],"population":[59],"totals.":[60],"The":[61],"output":[62],"single":[65],"an":[67],"office":[68],"publishes":[69],"once":[70],"applies":[72],"indicator":[75],"domain,":[77],"disseminated":[78],"replicate":[80],"weights":[81],"whose":[82],"variance":[83],"propagates":[84],"whole":[86],"estimated":[87,120,146],"pipeline.":[88],"With":[89],"linear":[90,108],"working":[91],"models":[92],"exact":[93],"consistency":[94],"faces":[95],"rank":[97],"ceiling;":[98],"escaping":[99],"it":[100],"needs":[101],"predictions":[102],"do":[104],"not":[105],"share":[106],"span,":[109],"through":[110],"adaptive":[111],"learners":[112],"or":[113],"cross-fitting.":[114],"Because":[115],"targets":[118,142],"on":[121,168,193],"reference":[123],"survey,":[124],"whether":[125],"sampling":[127],"error":[128,132,148],"enters":[129],"standard":[131,147],"choice,":[135],"consequential":[138],"one:":[139],"holding":[140],"those":[141],"fixed":[143],"divides":[144],"up":[150],"3.7":[152],"takes":[154],"coverage":[156],"of":[157],"nominal":[159],"95":[160],"%":[161,165],"interval":[162],"42":[164],".":[166],"Simulations":[167],"real":[169],"household-survey":[170],"microdata":[171],"price":[172],"sharing:":[174],"binding":[176],"limit":[177],"set":[179],"failure":[182],"mode":[183],"rather":[184],"than":[185],"alone,":[188],"cost":[191],"concentrates":[192],"rarest":[195],"indicator.":[196]},"counts_by_year":[],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2026-10-29T00:00:00"}