{"id":"https://openalex.org/W2787650400","doi":"https://doi.org/10.1109/ciss.2018.8362200","title":"Linearized binary regression","display_name":"Linearized binary regression","publication_year":2018,"publication_date":"2018-03-01","ids":{"openalex":"https://openalex.org/W2787650400","doi":"https://doi.org/10.1109/ciss.2018.8362200","mag":"2787650400"},"language":"en","primary_location":{"id":"doi:10.1109/ciss.2018.8362200","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ciss.2018.8362200","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 52nd Annual Conference on Information Sciences and Systems (CISS)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1802.00430","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5063813962","display_name":"Andrew Lan","orcid":"https://orcid.org/0000-0002-8475-6600"},"institutions":[{"id":"https://openalex.org/I20089843","display_name":"Princeton University","ror":"https://ror.org/00hx57361","country_code":"US","type":"education","lineage":["https://openalex.org/I20089843"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Andrew S. Lan","raw_affiliation_strings":["Princeton University, Princeton, NJ","Princeton Univ., Princeton , NJ#TAB#"],"affiliations":[{"raw_affiliation_string":"Princeton University, Princeton, NJ","institution_ids":["https://openalex.org/I20089843"]},{"raw_affiliation_string":"Princeton Univ., Princeton , NJ#TAB#","institution_ids":["https://openalex.org/I20089843"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110782105","display_name":"Mung Chiang","orcid":"https://orcid.org/0000-0002-8920-651X"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mung Chiang","raw_affiliation_strings":["Purdue University, West Lafayette, IN","Purdue University , West Lafayette, IN"],"affiliations":[{"raw_affiliation_string":"Purdue University, West Lafayette, IN","institution_ids":["https://openalex.org/I219193219"]},{"raw_affiliation_string":"Purdue University , West Lafayette, IN","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5083617223","display_name":"Christoph Studer","orcid":"https://orcid.org/0000-0001-8950-6267"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Christoph Studer","raw_affiliation_strings":["Cornell University, Ithaca, NY","[Cornell Univ., Ithaca, NY]"],"affiliations":[{"raw_affiliation_string":"Cornell University, Ithaca, NY","institution_ids":["https://openalex.org/I205783295"]},{"raw_affiliation_string":"[Cornell Univ., Ithaca, NY]","institution_ids":["https://openalex.org/I205783295"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5063813962"],"corresponding_institution_ids":["https://openalex.org/I20089843"],"apc_list":null,"apc_paid":null,"fwci":0.62869018,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.63653382,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9986000061035156,"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/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9986000061035156,"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/T10136","display_name":"Statistical Methods and Inference","score":0.9941999912261963,"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/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.9929999709129333,"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/probit-model","display_name":"Probit model","score":0.6615424752235413},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.6609175801277161},{"id":"https://openalex.org/keywords/probit","display_name":"Probit","score":0.5706273913383484},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.5567771196365356},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.5527507066726685},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.522931694984436},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.5180009007453918},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4754597544670105},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4676100015640259},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.45858049392700195},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.45026639103889465},{"id":"https://openalex.org/keywords/nonlinear-regression","display_name":"Nonlinear regression","score":0.4357798099517822},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.4244033694267273},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3564144968986511},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2857001721858978},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.2020980417728424}],"concepts":[{"id":"https://openalex.org/C67257552","wikidata":"https://www.wikidata.org/wiki/Q635217","display_name":"Probit model","level":2,"score":0.6615424752235413},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6609175801277161},{"id":"https://openalex.org/C184314375","wikidata":"https://www.wikidata.org/wiki/Q3117995","display_name":"Probit","level":2,"score":0.5706273913383484},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.5567771196365356},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.5527507066726685},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.522931694984436},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.5180009007453918},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4754597544670105},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4676100015640259},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.45858049392700195},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.45026639103889465},{"id":"https://openalex.org/C46889948","wikidata":"https://www.wikidata.org/wiki/Q2755024","display_name":"Nonlinear regression","level":3,"score":0.4357798099517822},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.4244033694267273},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3564144968986511},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2857001721858978},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.2020980417728424},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.0}],"mesh":[],"locations_count":6,"locations":[{"id":"doi:10.1109/ciss.2018.8362200","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ciss.2018.8362200","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 52nd Annual Conference on Information Sciences and Systems (CISS)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1802.00430","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1802.00430","pdf_url":"https://arxiv.org/pdf/1802.00430","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":"","raw_type":null},{"id":"mag:2787650400","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1802.00430.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":"arXiv (Cornell University)","raw_type":null},{"id":"pmh:oai:www.research-collection.ethz.ch:20.500.11850/455595","is_oa":true,"landing_page_url":"http://hdl.handle.net/20.500.11850/455595","pdf_url":null,"source":{"id":"https://openalex.org/S4306402302","display_name":"Repository for Publications and Research Data (ETH Zurich)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I35440088","host_organization_name":"ETH Zurich","host_organization_lineage":["https://openalex.org/I35440088"],"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":"2018 52nd Annual Conference on Information Sciences and Systems (CISS)","raw_type":"info:eu-repo/semantics/conferenceObject"},{"id":"doi:10.48550/arxiv.1802.00430","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1802.00430","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":null,"raw_source_name":null,"raw_type":"article"},{"id":"doi:10.3929/ethz-b-000455595","is_oa":true,"landing_page_url":"https://doi.org/10.3929/ethz-b-000455595","pdf_url":null,"source":{"id":"https://openalex.org/S7407051236","display_name":"ETH Z\u00fcrich Research Collection","issn_l":null,"issn":[],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1802.00430","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1802.00430","pdf_url":"https://arxiv.org/pdf/1802.00430","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":"","raw_type":null},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.8399999737739563,"display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W590507843","https://openalex.org/W1480376833","https://openalex.org/W1535520578","https://openalex.org/W1558747506","https://openalex.org/W1579303078","https://openalex.org/W1973948212","https://openalex.org/W1979421731","https://openalex.org/W1993838378","https://openalex.org/W2000483484","https://openalex.org/W2054640142","https://openalex.org/W2088844710","https://openalex.org/W2096451472","https://openalex.org/W2108306139","https://openalex.org/W2126917700","https://openalex.org/W2128208871","https://openalex.org/W2131725398","https://openalex.org/W2144073719","https://openalex.org/W2150579376","https://openalex.org/W2151499551","https://openalex.org/W2163599171","https://openalex.org/W2498119267","https://openalex.org/W2526144952","https://openalex.org/W2903950532","https://openalex.org/W2964200481","https://openalex.org/W2964322027","https://openalex.org/W3029645440","https://openalex.org/W3098888484","https://openalex.org/W3105629641","https://openalex.org/W4237450968","https://openalex.org/W6617492364","https://openalex.org/W6620498942","https://openalex.org/W6678614127"],"related_works":["https://openalex.org/W2962740320","https://openalex.org/W2763089577","https://openalex.org/W2148710984","https://openalex.org/W2889969537","https://openalex.org/W2072717728","https://openalex.org/W2805157711","https://openalex.org/W2292927606","https://openalex.org/W3024240631","https://openalex.org/W2093208925","https://openalex.org/W2921014221","https://openalex.org/W2782560798","https://openalex.org/W1981307074","https://openalex.org/W1981088744","https://openalex.org/W2341324644","https://openalex.org/W1654200","https://openalex.org/W2278809248","https://openalex.org/W2035978968","https://openalex.org/W2775234609","https://openalex.org/W2963916324","https://openalex.org/W3202227473"],"abstract_inverted_index":{"Probit":[0],"regression":[1,30,51,72,88,111,141],"was":[2],"first":[3],"proposed":[4],"by":[5],"Bliss":[6],"in":[7,36,61,145],"1934":[8],"to":[9,47,117],"study":[10],"mortality":[11],"rates":[12],"of":[13,20,86,101,123,131,148],"insects.":[14],"Since":[15],"then,":[16],"an":[17],"extensive":[18],"body":[19],"work":[21],"has":[22],"analyzed":[23],"and":[24,39,94,126,133,153],"used":[25],"probit":[26,59],"or":[27,78,162],"related":[28],"binary":[29,50,140],"methods":[31,112,125],"(such":[32],"as":[33,75],"logistic":[34],"regression)":[35],"numerous":[37],"applications":[38,156],"fields.":[40],"This":[41],"paper":[42],"provides":[43],"a":[44,83,129,146],"fresh":[45],"angle":[46],"such":[48,74],"well-established":[49],"methods.":[52],"Concretely,":[53],"we":[54],"demonstrate":[55],"that":[56,113,138,157],"linearizing":[57],"the":[58,98,121],"model":[60],"combination":[62],"with":[63,69,159],"linear":[64],"estimators":[65],"performs":[66],"on":[67],"par":[68],"state-of-the-art":[70],"nonlinear":[71,110],"methods,":[73],"posterior":[76],"mean":[77],"maximum":[79],"aposteriori":[80],"estimation,":[81,150],"for":[82,97,128],"broad":[84],"range":[85],"real-world":[87,134],"problems.":[89],"We":[90,119],"derive":[91],"exact,":[92],"closed-form,":[93],"nonasymptotic":[95],"expressions":[96],"mean-squared":[99],"error":[100],"our":[102,124],"linearized":[103,139],"estimators,":[104],"which":[105,136],"clearly":[106],"separates":[107],"them":[108],"from":[109],"are":[114],"typically":[115],"difficult":[116],"analyze.":[118],"showcase":[120],"efficacy":[122],"results":[127],"number":[130],"synthetic":[132],"datasets,":[135],"demonstrates":[137],"finds":[142],"potential":[143],"use":[144],"variety":[147],"inference,":[149],"signal":[151],"processing,":[152],"machine":[154],"learning":[155],"deal":[158],"binary-valued":[160],"observations":[161],"measurements.":[163]},"counts_by_year":[{"year":2018,"cited_by_count":2}],"updated_date":"2026-02-09T09:26:11.010843","created_date":"2025-10-10T00:00:00"}
