{"id":"https://openalex.org/W7021065929","doi":null,"title":"Non response weighting adjustment using response probability estimated via kernel regression methods","display_name":"Non response weighting adjustment using response probability estimated via kernel regression methods","publication_year":2009,"publication_date":"2009-01-01","ids":{"openalex":"https://openalex.org/W7021065929"},"language":"en","primary_location":{"id":"pmh:oai:flore.unifi.it:2158/371611","doi":null,"is_oa":false,"landing_page_url":"http://hdl.handle.net/2158/371611","pdf_url":null,"source":{"id":"https://openalex.org/S4306402033","display_name":"Florence Research (University of Florence)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I45084792","host_organization_name":"University of Florence","host_organization_lineage":["https://openalex.org/I45084792"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/conferenceObject"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":[],"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/A5023226367","display_name":"Andrea Giommi","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"GIOMMI, ANDREA","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5060961116","display_name":"Emilia Rocco","orcid":"https://orcid.org/0000-0003-4267-0734","observed_orcids":["https://orcid.org/0000-0003-4267-0734"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"ROCCO, EMILIA","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":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.57055749,"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":true,"primary_topic":{"id":"https://openalex.org/T13030","display_name":"Survey Sampling and Estimation Techniques","score":0.9945999979972839,"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":0.9945999979972839,"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":0.0044999998062849045,"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":9.999999747378752e-05,"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/unit-nonresponse","display_name":"unit nonresponse","score":0.8709999918937683},{"id":"https://openalex.org/keywords/weighting-adjustment","display_name":"weighting adjustment","score":0.7670000195503235},{"id":"https://openalex.org/keywords/kernel-regression","display_name":"kernel regression","score":0.9679999947547913},{"id":"https://openalex.org/keywords/nonparametric-estimation","display_name":"nonparametric estimation","score":0.7620000243186951},{"id":"https://openalex.org/keywords/double-sampling","display_name":"double sampling","score":0.7979999780654907},{"id":"https://openalex.org/keywords/nonresponse-bias","display_name":"nonresponse bias","score":0.7149999737739563},{"id":"https://openalex.org/keywords/small-sample-properties","display_name":"small sample properties","score":0.8840000033378601},{"id":"https://openalex.org/keywords/auxiliary-variables","display_name":"auxiliary variables","score":0.7179999947547913}],"concepts":[{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.7045999765396118},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.7020999789237976},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.6940000057220459},{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.5950999855995178},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.5946999788284302},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.5231000185012817},{"id":"https://openalex.org/C2779915747","wikidata":"https://www.wikidata.org/wiki/Q17058619","display_name":"Inverse probability weighting","level":3,"score":0.4945000112056732},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.46070000529289246},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.4560000002384186},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4092999994754791},{"id":"https://openalex.org/C134801348","wikidata":"https://www.wikidata.org/wiki/Q7140425","display_name":"Non-response bias","level":2,"score":0.4065000116825104},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.3865000009536743},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.34929999709129333},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.34709998965263367},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.31470000743865967},{"id":"https://openalex.org/C200695384","wikidata":"https://www.wikidata.org/wiki/Q1739319","display_name":"Kernel regression","level":3,"score":0.3116999864578247},{"id":"https://openalex.org/C71134354","wikidata":"https://www.wikidata.org/wiki/Q458825","display_name":"Kernel density estimation","level":3,"score":0.30570000410079956},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.3052000105381012},{"id":"https://openalex.org/C74127309","wikidata":"https://www.wikidata.org/wiki/Q3455886","display_name":"Nonparametric regression","level":3,"score":0.3050999939441681},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.29109999537467957},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.28459998965263367},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.27480000257492065},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.2671000063419342},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.2605000138282776},{"id":"https://openalex.org/C75373757","wikidata":"https://www.wikidata.org/wiki/Q7410160","display_name":"Sampling design","level":3,"score":0.25270000100135803},{"id":"https://openalex.org/C24574437","wikidata":"https://www.wikidata.org/wiki/Q7135228","display_name":"Parametric model","level":3,"score":0.2524000108242035}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:flore.unifi.it:2158/371611","doi":null,"is_oa":false,"landing_page_url":"http://hdl.handle.net/2158/371611","pdf_url":null,"source":{"id":"https://openalex.org/S4306402033","display_name":"Florence Research (University of Florence)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I45084792","host_organization_name":"University of Florence","host_organization_lineage":["https://openalex.org/I45084792"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W84240531","https://openalex.org/W7047869831","https://openalex.org/W215907939","https://openalex.org/W1602227246","https://openalex.org/W2075601854","https://openalex.org/W110811938","https://openalex.org/W3021200516","https://openalex.org/W2922139309","https://openalex.org/W3195151623","https://openalex.org/W7053074327"],"abstract_inverted_index":{"It":[0,14],"is":[1,7,33,56,126,196],"well":[2],"known":[3],"that":[4,137,220],"unit":[5,41],"nonresponse":[6,106],"a":[8,34,59,148],"common":[9],"problem":[10],"in":[11,78,217],"sample":[12],"surveys.":[13],"has":[15],"the":[16,21,24,45,52,64,72,76,79,84,87,91,127,138,144,186,202,210],"potential":[17],"to":[18,27,38,44,103,143,197],"bias":[19],"significantly":[20],"results":[22],"of":[23,54,75,86,129,185,193,205,209],"survey":[25],"and":[26,120,167,174,224],"prevent":[28],"valid":[29],"inference.":[30],"Weighing":[31],"adjustment":[32,68],"method":[35],"usually":[36,96],"applied":[37],"compensate":[39],"for":[40,47,50,105],"nonresponse.":[42],"According":[43],"theory":[46],"two-phase":[48],"sampling,":[49],"which":[51,134],"set":[53],"respondents":[55,77],"treated":[57],"as":[58],"second":[60],"phase":[61],"sampling":[62,73],"from":[63],"original":[65],"sample,":[66,80],"weighting":[67,188],"operates":[69],"by":[70,83,113,131,147,158,163,171],"increasing":[71],"weights":[74],"multiplying":[81],"them":[82,130],"inverse":[85],"response":[88,93,99,109,118,139,211],"probabilities.":[89],"Since":[90],"true":[92],"probabilities":[94,100,110,140,212],"are":[95,101,111,221],"unknown,":[97],"estimated":[98,112],"used":[102],"correct":[104],"bias.":[107],"Typically,":[108],"fitting":[114],"parametric":[115],"models":[116],"relating":[117],"occurrences":[119],"auxiliary":[121,145,214],"variables.":[122],"An":[123],"alternative":[124],"solution":[125],"estimation":[128,208],"nonparametric":[132],"methods":[133],"require":[135],"only":[136],"be":[141],"related":[142],"variables":[146],"smooth":[149],"but":[150],"unspecified":[151],"function.":[152],"This":[153],"possibility":[154],"was":[155],"first":[156],"proposed":[157],"Giommi":[159],"(1984)":[160],"then":[161],"considered":[162],"Niyonsenga":[164],"(1994,":[165],"1997)":[166],"recently":[168],"further":[169],"investigated":[170],"Da":[172],"Silva":[173],"Opsomer":[175],"(2006,":[176],"2008)":[177],"who":[178],"derived":[179],"some":[180],"new":[181],"important":[182],"theoretical":[183],"properties":[184,204],"non-parametrically":[187],"adjusted":[189],"estimators.":[190],"The":[191],"aim":[192],"this":[194],"paper":[195],"investigate,":[198],"via":[199],"simulation":[200],"experiments,":[201],"small-sample":[203],"kernel":[206],"regression":[207],"when":[213],"information":[215],"consists":[216],"multiple":[218],"covariates":[219],"both":[222],"categorical":[223],"continuous.":[225]},"counts_by_year":[],"updated_date":"2026-10-13T10:08:12.033832","created_date":"2025-10-10T00:00:00"}