{"id":"https://openalex.org/W7214790469","doi":"https://doi.org/10.48550/arxiv.2609.34959","title":"A Joint Sampling Design for the Related Populations of Parents and Children","display_name":"A Joint Sampling Design for the Related Populations of Parents and Children","publication_year":2026,"publication_date":"2026-09-28","ids":{"openalex":"https://openalex.org/W7214790469","doi":"https://doi.org/10.48550/arxiv.2609.34959"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2609.34959","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.34959","pdf_url":"https://arxiv.org/pdf/2609.34959","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2609.34959","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5007614437","display_name":"Yves Till\u00e9","orcid":"https://orcid.org/0000-0003-0904-5523","observed_orcids":["https://orcid.org/0000-0003-0904-5523"]},"institutions":[{"id":"https://openalex.org/I57825437","display_name":"University of Neuch\u00e2tel","ror":"https://ror.org/00vasag41","country_code":"CH","type":"education","lineage":["https://openalex.org/I57825437"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Till\u00e9, Yves","raw_affiliation_strings":["Institut de statistique , Universit\u00e9 de Neuch\u00e2tel . Switzerland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institut de statistique , Universit\u00e9 de Neuch\u00e2tel . Switzerland","institution_ids":["https://openalex.org/I57825437"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5153504358","display_name":"Mar\u00eda Guadarrama Sanz","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4210148621","display_name":"Luxembourg Institute of Socio-Economic Research","ror":"https://ror.org/040jf9322","country_code":"LU","type":"nonprofit","lineage":["https://openalex.org/I4210148621"]}],"countries":["LU"],"is_corresponding":false,"raw_author_name":"Sanz, Mar\u00eda Guadarrama","raw_affiliation_strings":["Luxembourg Institute of Socio-Economic Research (LISER) . Address: 11 , Porte des Sciences , L-4366 Esch-sur.Alzette/Belval ( Luxembourg)","Luxembourg Institute of Socio-Economic Research , Luxembourg"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Luxembourg Institute of Socio-Economic Research (LISER) . Address: 11 , Porte des Sciences , L-4366 Esch-sur.Alzette/Belval ( Luxembourg)","institution_ids":["https://openalex.org/I4210148621"]},{"raw_affiliation_string":"Luxembourg Institute of Socio-Economic Research , Luxembourg","institution_ids":["https://openalex.org/I4210148621"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"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":0.9984999895095825,"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":0.9984999895095825,"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":0.0005000000237487257,"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/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"}}],"keywords":[{"id":"https://openalex.org/keywords/balanced-sampling","display_name":"balanced sampling","score":0.6700000166893005},{"id":"https://openalex.org/keywords/stepfamilies","display_name":"stepfamilies","score":0.671999990940094},{"id":"https://openalex.org/keywords/shared-parental-responsibility","display_name":"shared parental responsibility","score":0.7910000085830688},{"id":"https://openalex.org/keywords/unequal-probability-sampling","display_name":"unequal probability sampling","score":0.6510000228881836},{"id":"https://openalex.org/keywords/auxiliary-information","display_name":"auxiliary information","score":0.4950000047683716},{"id":"https://openalex.org/keywords/survey-weighting","display_name":"survey weighting","score":0.6309999823570251}],"concepts":[{"id":"https://openalex.org/C75373757","wikidata":"https://www.wikidata.org/wiki/Q7410160","display_name":"Sampling design","level":3,"score":0.6916999816894531},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.6705999970436096},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.6690000295639038},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6050000190734863},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.5382000207901001},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.5181999802589417},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4706999957561493},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.420199990272522},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4138999879360199},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.41350001096725464},{"id":"https://openalex.org/C82152865","wikidata":"https://www.wikidata.org/wiki/Q7208505","display_name":"Poisson sampling","level":5,"score":0.38339999318122864},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.3160000145435333},{"id":"https://openalex.org/C75917345","wikidata":"https://www.wikidata.org/wiki/Q2725298","display_name":"Sampling bias","level":3,"score":0.313400000333786},{"id":"https://openalex.org/C20353970","wikidata":"https://www.wikidata.org/wiki/Q1056998","display_name":"Simple random sample","level":3,"score":0.3068999946117401},{"id":"https://openalex.org/C2779318504","wikidata":"https://www.wikidata.org/wiki/Q1438035","display_name":"Research design","level":2,"score":0.30390000343322754},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3009999990463257},{"id":"https://openalex.org/C2993010571","wikidata":"https://www.wikidata.org/wiki/Q49908","display_name":"Probability sampling","level":3,"score":0.29840001463890076},{"id":"https://openalex.org/C17648541","wikidata":"https://www.wikidata.org/wiki/Q2265984","display_name":"Efficiency","level":3,"score":0.27720001339912415},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2754000127315521},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.26980000734329224},{"id":"https://openalex.org/C139134214","wikidata":"https://www.wikidata.org/wiki/Q3652044","display_name":"Multistage sampling","level":2,"score":0.26809999346733093},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.26499998569488525},{"id":"https://openalex.org/C18653775","wikidata":"https://www.wikidata.org/wiki/Q1333358","display_name":"Joint probability distribution","level":2,"score":0.2632000148296356}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2609.34959","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.34959","pdf_url":"https://arxiv.org/pdf/2609.34959","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"},{"id":"doi:10.48550/arxiv.2609.34959","doi":"https://doi.org/10.48550/arxiv.2609.34959","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2609.34959","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2609.34959","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.34959","pdf_url":"https://arxiv.org/pdf/2609.34959","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7214790469.pdf","grobid_xml":"https://content.openalex.org/works/W7214790469.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2272310236","https://openalex.org/W2571982639","https://openalex.org/W2985974912","https://openalex.org/W3093256252","https://openalex.org/W4407835807","https://openalex.org/W1529165716","https://openalex.org/W7221132298","https://openalex.org/W2766370547","https://openalex.org/W3120710608","https://openalex.org/W3125438780"],"abstract_inverted_index":{"We":[0],"propose":[1],"a":[2,34],"novel":[3],"joint":[4],"sampling":[5,50,74,143],"design":[6,32],"for":[7,65,103,126],"surveys":[8],"involving":[9],"two":[10],"interrelated":[11],"populations":[12],"of":[13,20,37,140],"parents":[14],"and":[15,28,69,89,130,138],"children":[16],"in":[17,47],"the":[18,48,67,109,115,122,135,141],"presence":[19],"complex":[21],"family":[22,97],"structures,":[23],"including":[24],"shared":[25],"parental":[26],"responsibility":[27],"blended":[29],"families.":[30],"The":[31,92],"combines":[33],"graph-theoretic":[35],"representation":[36],"co-parenting":[38],"relationships":[39,98],"with":[40],"balanced":[41],"sampling.":[42],"This":[43],"framework":[44,94],"ensures":[45],"that,":[46],"first":[49],"phase,":[51],"exactly":[52],"one":[53,77],"parent":[54,68,81],"is":[55],"selected":[56],"from":[57],"each":[58],"co-responsibility":[59],"pair":[60],"while":[61,99],"maintaining":[62],"accurate":[63],"inference":[64],"both":[66,104],"child":[70,78],"populations.":[71,105],"A":[72],"second":[73],"phase":[75],"selects":[76],"per":[79],"sampled":[80],"using":[82],"unequal":[83],"probabilities":[84],"that":[85,114],"produce":[86],"consistent":[87],"weighting":[88],"unbiased":[90],"estimation.":[91],"proposed":[93,142],"accommodates":[95],"overlapping":[96],"balancing":[100,128],"auxiliary":[101,132],"information":[102],"Results":[106],"based":[107],"on":[108],"Luxembourg":[110],"population":[111,124],"registers":[112],"show":[113],"estimated":[116],"totals":[117,125],"are":[118],"virtually":[119],"identical":[120],"to":[121],"known":[123],"all":[127],"variables,":[129,133],"other":[131],"demonstrating":[134],"excellent":[136],"precision":[137],"efficiency":[139],"design.":[144]},"counts_by_year":[],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2026-09-30T00:00:00"}
