{"id":"https://openalex.org/W2808596299","doi":"https://doi.org/10.1080/10485252.2018.1483501","title":"Single-index regression for pooled biomarker data","display_name":"Single-index regression for pooled biomarker data","publication_year":2018,"publication_date":"2018-06-14","ids":{"openalex":"https://openalex.org/W2808596299","doi":"https://doi.org/10.1080/10485252.2018.1483501","mag":"2808596299"},"language":"en","primary_location":{"id":"doi:10.1080/10485252.2018.1483501","doi":"https://doi.org/10.1080/10485252.2018.1483501","is_oa":false,"landing_page_url":"https://doi.org/10.1080/10485252.2018.1483501","pdf_url":null,"source":{"id":"https://openalex.org/S157569606","display_name":"Journal of nonparametric statistics","issn_l":"1026-7654","issn":["1026-7654","1029-0311","1048-5252"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["abdc-b","anvur-class-a","anvur-scientific","cwts-core","dongbi-d","jufo-1","norway-1","poland-70","russia-white-list-3","vabb-shw"],"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547","https://openalex.org/P4310320449"],"host_organization_lineage_names":["Taylor & Francis","Informa"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Nonparametric Statistics","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://figshare.com/articles/journal_contribution/Single-index_regression_for_pooled_biomarker_data/6541172","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5045769121","display_name":"Juexin Lin","orcid":"https://orcid.org/0000-0001-7549-4031","observed_orcids":["https://orcid.org/0000-0001-7549-4031"]},"institutions":[{"id":"https://openalex.org/I155781252","display_name":"University of South Carolina","ror":"https://ror.org/02b6qw903","country_code":"US","type":"education","lineage":["https://openalex.org/I155781252"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Juexin Lin","raw_affiliation_strings":["Department of Statistics, University of South Carolina, Columbia, SC, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, University of South Carolina, Columbia, SC, USA","institution_ids":["https://openalex.org/I155781252"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044668868","display_name":"Dewei Wang","orcid":"https://orcid.org/0000-0003-0822-8563","observed_orcids":["https://orcid.org/0000-0003-0822-8563"]},"institutions":[{"id":"https://openalex.org/I155781252","display_name":"University of South Carolina","ror":"https://ror.org/02b6qw903","country_code":"US","type":"education","lineage":["https://openalex.org/I155781252"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Dewei Wang","raw_affiliation_strings":["Department of Statistics, University of South Carolina, Columbia, SC, USA"],"raw_orcid":"https://orcid.org/0000-0003-0822-8563","affiliations":[{"raw_affiliation_string":"Department of Statistics, University of South Carolina, Columbia, SC, USA","institution_ids":["https://openalex.org/I155781252"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5044668868"],"corresponding_institution_ids":["https://openalex.org/I155781252"],"apc_list":null,"apc_paid":null,"fwci":0.8329,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.75680161,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":"30","issue":"4","first_page":"813","last_page":"833"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.926800012588501,"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/T10136","display_name":"Statistical Methods and Inference","score":0.926800012588501,"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/T14374","display_name":"Statistical Methods in Epidemiology","score":0.024000000208616257,"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/T13141","display_name":"Statistical Methods and Applications","score":0.009499999694526196,"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/single-index-model","display_name":"single index model","score":0.753000020980835},{"id":"https://openalex.org/keywords/semiparametric-regression","display_name":"semiparametric regression","score":0.9470000267028809},{"id":"https://openalex.org/keywords/laboratory-cost-reduction","display_name":"laboratory cost reduction","score":0.49900001287460327},{"id":"https://openalex.org/keywords/asymptotic-properties","display_name":"asymptotic properties","score":0.6570000052452087},{"id":"https://openalex.org/keywords/nonparametric-estimation","display_name":"nonparametric estimation","score":0.6159999966621399},{"id":"https://openalex.org/keywords/diabetes-mellitus","display_name":"diabetes mellitus","score":0.7039999961853027},{"id":"https://openalex.org/keywords/chemokines","display_name":"chemokines","score":0.9190000295639038}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.698648989200592},{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.6302101016044617},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5945459604263306},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.5667755007743835},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5657390356063843},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.5368732810020447},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4875316321849823},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.46938344836235046},{"id":"https://openalex.org/C2777382242","wikidata":"https://www.wikidata.org/wiki/Q6017816","display_name":"Index (typography)","level":2,"score":0.4248817265033722},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.40872013568878174},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.37194788455963135},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2801074683666229},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.25202035903930664},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.21924415230751038},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1080/10485252.2018.1483501","doi":"https://doi.org/10.1080/10485252.2018.1483501","is_oa":false,"landing_page_url":"https://doi.org/10.1080/10485252.2018.1483501","pdf_url":null,"source":{"id":"https://openalex.org/S157569606","display_name":"Journal of nonparametric statistics","issn_l":"1026-7654","issn":["1026-7654","1029-0311","1048-5252"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["abdc-b","anvur-class-a","anvur-scientific","cwts-core","dongbi-d","jufo-1","norway-1","poland-70","russia-white-list-3","vabb-shw"],"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547","https://openalex.org/P4310320449"],"host_organization_lineage_names":["Taylor & Francis","Informa"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Nonparametric Statistics","raw_type":"journal-article"},{"id":"pmh:oai:RePEc:taf:gnstxx:v:30:y:2018:i:4:p:813-833","doi":null,"is_oa":false,"landing_page_url":"http://hdl.handle.net/10.1080/10485252.2018.1483501","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":null,"raw_type":"article"},{"id":"pmh:oai:figshare.com:article/6541172","doi":null,"is_oa":true,"landing_page_url":"https://figshare.com/articles/journal_contribution/Single-index_regression_for_pooled_biomarker_data/6541172","pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Text"},{"id":"doi:10.6084/m9.figshare.6541172.v1","doi":"https://doi.org/10.6084/m9.figshare.6541172.v1","is_oa":true,"landing_page_url":"https://doi.org/10.6084/m9.figshare.6541172.v1","pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"pmh:oai:figshare.com:article/6541172","doi":null,"is_oa":true,"landing_page_url":"https://figshare.com/articles/journal_contribution/Single-index_regression_for_pooled_biomarker_data/6541172","pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Text"},"sustainable_development_goals":[],"sustainable_development_goals_aurora":[{"id":"https://metadata.un.org/sdg/3","score":0.800000011920929,"display_name":"Good health and well-being"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W1577630157","https://openalex.org/W1731548826","https://openalex.org/W1884654759","https://openalex.org/W1972320266","https://openalex.org/W1975617028","https://openalex.org/W1975680434","https://openalex.org/W1976421552","https://openalex.org/W1978930761","https://openalex.org/W1984280847","https://openalex.org/W1984715138","https://openalex.org/W1985539148","https://openalex.org/W2003019434","https://openalex.org/W2027018872","https://openalex.org/W2027244596","https://openalex.org/W2029219288","https://openalex.org/W2031674496","https://openalex.org/W2032063120","https://openalex.org/W2038784387","https://openalex.org/W2041071132","https://openalex.org/W2043590435","https://openalex.org/W2059745320","https://openalex.org/W2060231208","https://openalex.org/W2064987142","https://openalex.org/W2071139882","https://openalex.org/W2077083909","https://openalex.org/W2079829847","https://openalex.org/W2080749331","https://openalex.org/W2087335160","https://openalex.org/W2113322067","https://openalex.org/W2113439767","https://openalex.org/W2121545841","https://openalex.org/W2123654848","https://openalex.org/W2136175429","https://openalex.org/W2136239288","https://openalex.org/W2146968919","https://openalex.org/W2147679820","https://openalex.org/W2147904256","https://openalex.org/W2148598270","https://openalex.org/W2161716547","https://openalex.org/W2163875075","https://openalex.org/W2164416177","https://openalex.org/W2171050905","https://openalex.org/W2180334972","https://openalex.org/W2296677229","https://openalex.org/W2315196079","https://openalex.org/W2324339700","https://openalex.org/W4302414222"],"related_works":["https://openalex.org/W1976421552","https://openalex.org/W2598512440","https://openalex.org/W1577630157","https://openalex.org/W7100694860","https://openalex.org/W2296677229","https://openalex.org/W7099943488","https://openalex.org/W1993478216","https://openalex.org/W2129194112","https://openalex.org/W2291127084","https://openalex.org/W2605247181"],"abstract_inverted_index":{"Laboratory":[0],"assays":[1],"used":[2,28],"to":[3,18,38,43,134],"evaluate":[4],"biomarkers":[5],"(biological":[6],"markers)":[7],"are":[8,91,117],"often":[9],"prohibitively":[10],"expensive.":[11],"As":[12],"an":[13],"efficient":[14],"data":[15,90,137,142],"collection":[16],"mechanism":[17],"save":[19],"on":[20,94],"testing":[21],"costs,":[22],"pooling":[23],"has":[24],"become":[25],"more":[26],"commonly":[27],"in":[29,86],"epidemiological":[30],"research.":[31],"Useful":[32],"statistical":[33],"methods":[34],"have":[35,53],"been":[36],"proposed":[37],"relate":[39],"pooled":[40],"biomarker":[41,92],"measurements":[42,93],"individual":[44,98],"covariate":[45],"information.":[46],"However,":[47],"most":[48],"of":[49,73,114,125],"these":[50],"regression":[51],"techniques":[52],"proceeded":[54],"under":[55],"parametric":[56],"linear":[57],"assumptions.":[58],"To":[59],"relax":[60],"such":[61],"assumptions,":[62],"we":[63,82,103],"propose":[64,104],"a":[65,84,105,135,140],"semiparametric":[66],"approach":[67],"that":[68,87,107],"originates":[69],"from":[70],"the":[71,74,88,121],"context":[72],"single-index":[75,80],"model.":[76],"Unlike":[77],"with":[78],"traditional":[79],"methodologies,":[81],"face":[83],"challenge":[85],"observed":[89],"pools":[95],"rather":[96],"than":[97],"specimens.":[99],"In":[100],"this":[101,109],"article,":[102],"method":[106],"addresses":[108],"challenge.":[110],"The":[111],"asymptotic":[112],"properties":[113],"our":[115,126],"estimators":[116,127],"derived.":[118],"We":[119],"illustrate":[120],"finite":[122],"sample":[123],"performance":[124],"through":[128],"simulation":[129],"and":[130,139],"by":[131],"applying":[132],"it":[133],"diabetes":[136],"set":[138],"chemokine":[141],"set.":[143]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2018-06-21T00:00:00"}
