{"id":"https://openalex.org/W3097162844","doi":"https://doi.org/10.1080/10618600.2020.1844215","title":"LowCon: A Design-based Subsampling Approach in a Misspecified Linear Model","display_name":"LowCon: A Design-based Subsampling Approach in a Misspecified Linear Model","publication_year":2020,"publication_date":"2020-11-02","ids":{"openalex":"https://openalex.org/W3097162844","doi":"https://doi.org/10.1080/10618600.2020.1844215","mag":"3097162844"},"language":"en","primary_location":{"id":"doi:10.1080/10618600.2020.1844215","is_oa":false,"landing_page_url":"https://doi.org/10.1080/10618600.2020.1844215","pdf_url":null,"source":{"id":"https://openalex.org/S76159266","display_name":"Journal of Computational and Graphical Statistics","issn_l":"1061-8600","issn":["1061-8600","1537-2715"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computational and Graphical Statistics","raw_type":"journal-article"},"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/A5102923021","display_name":"Cheng Meng","orcid":"https://orcid.org/0000-0002-7111-0966"},"institutions":[{"id":"https://openalex.org/I4210096250","display_name":"Beijing Institute of Big Data Research","ror":"https://ror.org/00s1sz824","country_code":"CN","type":"facility","lineage":["https://openalex.org/I20231570","https://openalex.org/I37796252","https://openalex.org/I4210096250"]},{"id":"https://openalex.org/I78988378","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92","country_code":"CN","type":"education","lineage":["https://openalex.org/I78988378"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cheng Meng","raw_affiliation_strings":["Institute of Statistics and Big data, Renmin University of China","Institute of Statistics and Big data, Renmin University of China, Beijing, China;"],"affiliations":[{"raw_affiliation_string":"Institute of Statistics and Big data, Renmin University of China","institution_ids":["https://openalex.org/I78988378","https://openalex.org/I4210096250"]},{"raw_affiliation_string":"Institute of Statistics and Big data, Renmin University of China, Beijing, China;","institution_ids":["https://openalex.org/I78988378"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049832804","display_name":"Rui Xie","orcid":"https://orcid.org/0000-0001-9769-8131"},"institutions":[{"id":"https://openalex.org/I106165777","display_name":"University of Central Florida","ror":"https://ror.org/036nfer12","country_code":"US","type":"education","lineage":["https://openalex.org/I106165777"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rui Xie","raw_affiliation_strings":["Department of Statistics and Data Science, University of Central Florida","Department of Statistics and Data Science, University of Central Florida, Orlando, FL;"],"affiliations":[{"raw_affiliation_string":"Department of Statistics and Data Science, University of Central Florida","institution_ids":["https://openalex.org/I106165777"]},{"raw_affiliation_string":"Department of Statistics and Data Science, University of Central Florida, Orlando, FL;","institution_ids":["https://openalex.org/I106165777"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084419948","display_name":"Abhyuday Mandal","orcid":"https://orcid.org/0000-0001-9461-1483"},"institutions":[{"id":"https://openalex.org/I165733156","display_name":"University of Georgia","ror":"https://ror.org/00te3t702","country_code":"US","type":"education","lineage":["https://openalex.org/I165733156"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Abhyuday Mandal","raw_affiliation_strings":["Department of Statistics, University of Georgia","Department of Statistics, University of Georgia, Athens, GA;"],"affiliations":[{"raw_affiliation_string":"Department of Statistics, University of Georgia","institution_ids":["https://openalex.org/I165733156"]},{"raw_affiliation_string":"Department of Statistics, University of Georgia, Athens, GA;","institution_ids":["https://openalex.org/I165733156"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102927572","display_name":"Xinlian Zhang","orcid":"https://orcid.org/0000-0002-0913-1205"},"institutions":[{"id":"https://openalex.org/I36258959","display_name":"University of California San Diego","ror":"https://ror.org/0168r3w48","country_code":"US","type":"education","lineage":["https://openalex.org/I36258959"]},{"id":"https://openalex.org/I4210148697","display_name":"Institute of Bioinformatics","ror":"https://ror.org/04hqfvm50","country_code":"IN","type":"nonprofit","lineage":["https://openalex.org/I4210148697"]}],"countries":["IN","US"],"is_corresponding":false,"raw_author_name":"Xinlian Zhang","raw_affiliation_strings":["Division of Biostatistics and Bioinformatics, University of California","Division of Biostatistics and Bioinformatics, University of California, San Diego, CA"],"affiliations":[{"raw_affiliation_string":"Division of Biostatistics and Bioinformatics, University of California","institution_ids":["https://openalex.org/I4210148697"]},{"raw_affiliation_string":"Division of Biostatistics and Bioinformatics, University of California, San Diego, CA","institution_ids":["https://openalex.org/I36258959"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103135389","display_name":"Wenxuan Zhong","orcid":"https://orcid.org/0000-0001-9006-622X"},"institutions":[{"id":"https://openalex.org/I165733156","display_name":"University of Georgia","ror":"https://ror.org/00te3t702","country_code":"US","type":"education","lineage":["https://openalex.org/I165733156"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wenxuan Zhong","raw_affiliation_strings":["Department of Statistics, University of Georgia","Department of Statistics, University of Georgia, Athens, GA;"],"affiliations":[{"raw_affiliation_string":"Department of Statistics, University of Georgia","institution_ids":["https://openalex.org/I165733156"]},{"raw_affiliation_string":"Department of Statistics, University of Georgia, Athens, GA;","institution_ids":["https://openalex.org/I165733156"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100664656","display_name":"Ping Ma","orcid":"https://orcid.org/0000-0002-5728-3596"},"institutions":[{"id":"https://openalex.org/I165733156","display_name":"University of Georgia","ror":"https://ror.org/00te3t702","country_code":"US","type":"education","lineage":["https://openalex.org/I165733156"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Ping Ma","raw_affiliation_strings":["Department of Statistics, University of Georgia","Department of Statistics, University of Georgia, Athens, GA;"],"affiliations":[{"raw_affiliation_string":"Department of Statistics, University of Georgia","institution_ids":["https://openalex.org/I165733156"]},{"raw_affiliation_string":"Department of Statistics, University of Georgia, Athens, GA;","institution_ids":["https://openalex.org/I165733156"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100664656"],"corresponding_institution_ids":["https://openalex.org/I165733156"],"apc_list":null,"apc_paid":null,"fwci":3.0353,"has_fulltext":false,"cited_by_count":48,"citation_normalized_percentile":{"value":0.9299485,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"30","issue":"3","first_page":"694","last_page":"708"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9973000288009644,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9973000288009644,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11798","display_name":"Optimal Experimental Design Methods","score":0.9958999752998352,"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"}},{"id":"https://openalex.org/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.9936000108718872,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.7387315034866333},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.6009680032730103},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5978543758392334},{"id":"https://openalex.org/keywords/linear-model","display_name":"Linear model","score":0.434354692697525},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.39479076862335205},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.32687148451805115},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.32244306802749634},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.29843246936798096},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.24416899681091309}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.7387315034866333},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.6009680032730103},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5978543758392334},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.434354692697525},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39479076862335205},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.32687148451805115},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32244306802749634},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.29843246936798096},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.24416899681091309}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/10618600.2020.1844215","is_oa":false,"landing_page_url":"https://doi.org/10.1080/10618600.2020.1844215","pdf_url":null,"source":{"id":"https://openalex.org/S76159266","display_name":"Journal of Computational and Graphical Statistics","issn_l":"1061-8600","issn":["1061-8600","1537-2715"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computational and Graphical Statistics","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":49,"referenced_works":["https://openalex.org/W591076336","https://openalex.org/W1487825358","https://openalex.org/W1492735889","https://openalex.org/W1497443639","https://openalex.org/W1964971655","https://openalex.org/W1967692477","https://openalex.org/W1969545943","https://openalex.org/W1986491966","https://openalex.org/W2001331635","https://openalex.org/W2003553868","https://openalex.org/W2009086487","https://openalex.org/W2015616421","https://openalex.org/W2023312901","https://openalex.org/W2025347578","https://openalex.org/W2031963886","https://openalex.org/W2038669746","https://openalex.org/W2056594234","https://openalex.org/W2084744129","https://openalex.org/W2087794258","https://openalex.org/W2103368439","https://openalex.org/W2142606975","https://openalex.org/W2154735538","https://openalex.org/W2155544089","https://openalex.org/W2293401948","https://openalex.org/W2559655401","https://openalex.org/W2596535828","https://openalex.org/W2599189192","https://openalex.org/W2606846509","https://openalex.org/W2615055317","https://openalex.org/W2616345629","https://openalex.org/W2883253089","https://openalex.org/W2892780227","https://openalex.org/W3009587692","https://openalex.org/W3022172246","https://openalex.org/W3044420422","https://openalex.org/W3081894066","https://openalex.org/W3098603383","https://openalex.org/W3147833427","https://openalex.org/W4206723194","https://openalex.org/W4212774754","https://openalex.org/W4213283575","https://openalex.org/W4238306122","https://openalex.org/W4247680473","https://openalex.org/W4249517230","https://openalex.org/W4298876635","https://openalex.org/W4300031234","https://openalex.org/W4320800818","https://openalex.org/W4399523768","https://openalex.org/W6629856084"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4286629047","https://openalex.org/W3046775127","https://openalex.org/W3107602296","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W4313488044","https://openalex.org/W3209574120","https://openalex.org/W4312192474"],"abstract_inverted_index":{"We":[0,131],"consider":[1],"a":[2,34,62,68,101,128],"measurement":[3],"constrained":[4],"supervised":[5,47],"learning":[6,48],"problem,":[7],"that":[8,81,133],"is,":[9],"(i)":[10],"full":[11],"sample":[12],"of":[13,36,53,83],"the":[14,19,40,46,51,54,72,84,92,109,113,134,140,152,158],"predictors":[15,55],"are":[16,22,94,179],"given;":[17],"(ii)":[18],"response":[20],"observations":[21],"unavailable":[23],"and":[24,43,56,64,67,154],"expensive":[25],"to":[26,32,126,146],"measure.":[27],"Thus,":[28],"it":[29],"is":[30,61,117,161],"ideal":[31],"select":[33],"subsample":[35,52,166],"predictor":[37],"observations,":[38],"measure":[39],"corresponding":[41],"responses,":[42],"then":[44],"fit":[45],"model":[49,59,70,116],"on":[50],"responses.":[57],"However,":[58],"fitting":[60],"trial":[63],"error":[65],"process,":[66],"postulated":[69],"for":[71,176],"data":[73],"could":[74],"be":[75],"misspecified.":[76,95,118],"Our":[77,119],"empirical":[78],"studies":[79],"demonstrate":[80,157],"most":[82],"existing":[85],"subsampling":[86,103,172],"methods":[87,111],"have":[88],"unsatisfactory":[89],"performances":[90],"when":[91,112],"models":[93],"In":[96],"this":[97,177],"paper,":[98],"we":[99],"develop":[100],"novel":[102],"method,":[104],"called":[105],"\u201cLowCon,\u201d":[106],"which":[107],"outperforms":[108],"competing":[110],"working":[114],"linear":[115],"method":[120],"uses":[121],"orthogonal":[122],"Latin":[123],"hypercube":[124],"designs":[125],"achieve":[127],"robust":[129,163],"estimation.":[130],"show":[132],"proposed":[135,159],"design-based":[136],"estimator":[137,160],"approximately":[138],"minimizes":[139],"so-called":[141],"worst-case":[142],"bias":[143],"with":[144],"respect":[145],"many":[147],"possible":[148],"misspecification":[149],"terms.":[150],"Both":[151],"simulated":[153],"real-data":[155],"analyses":[156],"more":[162],"than":[164],"several":[165],"least-squares":[167],"estimators":[168],"obtained":[169],"by":[170],"state-of-the-art":[171],"methods.":[173],"Supplementary":[174],"materials":[175],"article":[178],"available":[180],"online.":[181]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":13},{"year":2023,"cited_by_count":12},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":1}],"updated_date":"2026-04-02T15:55:50.835912","created_date":"2025-10-10T00:00:00"}
