{"id":"https://openalex.org/W4387245336","doi":"https://doi.org/10.1080/10618600.2023.2260052","title":"Big Data Model Building Using Dimension Reduction and Sample Selection","display_name":"Big Data Model Building Using Dimension Reduction and Sample Selection","publication_year":2023,"publication_date":"2023-10-02","ids":{"openalex":"https://openalex.org/W4387245336","doi":"https://doi.org/10.1080/10618600.2023.2260052"},"language":"en","primary_location":{"id":"doi:10.1080/10618600.2023.2260052","is_oa":false,"landing_page_url":"https://doi.org/10.1080/10618600.2023.2260052","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/A5030657372","display_name":"Lih\u2010Yuan Deng","orcid":"https://orcid.org/0000-0002-7350-9751"},"institutions":[{"id":"https://openalex.org/I94658018","display_name":"University of Memphis","ror":"https://ror.org/01cq23130","country_code":"US","type":"education","lineage":["https://openalex.org/I94658018"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lih-Yuan Deng","raw_affiliation_strings":["Department of Mathematical Sciences, University of Memphis, Memphis, TN"],"affiliations":[{"raw_affiliation_string":"Department of Mathematical Sciences, University of Memphis, Memphis, TN","institution_ids":["https://openalex.org/I94658018"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086031117","display_name":"Ching\u2010Chi Yang","orcid":"https://orcid.org/0000-0003-2517-2709"},"institutions":[{"id":"https://openalex.org/I94658018","display_name":"University of Memphis","ror":"https://ror.org/01cq23130","country_code":"US","type":"education","lineage":["https://openalex.org/I94658018"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ching-Chi Yang","raw_affiliation_strings":["Department of Mathematical Sciences, University of Memphis, Memphis, TN"],"affiliations":[{"raw_affiliation_string":"Department of Mathematical Sciences, University of Memphis, Memphis, TN","institution_ids":["https://openalex.org/I94658018"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073224585","display_name":"Dale Bowman","orcid":"https://orcid.org/0000-0002-3874-5257"},"institutions":[{"id":"https://openalex.org/I94658018","display_name":"University of Memphis","ror":"https://ror.org/01cq23130","country_code":"US","type":"education","lineage":["https://openalex.org/I94658018"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dale Bowman","raw_affiliation_strings":["Department of Mathematical Sciences, University of Memphis, Memphis, TN"],"affiliations":[{"raw_affiliation_string":"Department of Mathematical Sciences, University of Memphis, Memphis, TN","institution_ids":["https://openalex.org/I94658018"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057669385","display_name":"Dennis K. J. Lin","orcid":"https://orcid.org/0000-0003-2552-7709"},"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":"Dennis K. J. Lin","raw_affiliation_strings":["Department of Statistics, Purdue University, West Lafayette, IN"],"affiliations":[{"raw_affiliation_string":"Department of Statistics, Purdue University, West Lafayette, IN","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5052673069","display_name":"Henry Horng\u2010Shing Lu","orcid":"https://orcid.org/0000-0002-4392-3361"},"institutions":[{"id":"https://openalex.org/I148366613","display_name":"National Yang Ming Chiao Tung University","ror":"https://ror.org/00se2k293","country_code":"TW","type":"education","lineage":["https://openalex.org/I148366613"]},{"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":["TW","US"],"is_corresponding":true,"raw_author_name":"Henry Horng-Shing Lu","raw_affiliation_strings":["Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu, Taiwan; Department of Statistics and Data Science, Cornell University, Ithaca, NY","Department of Statistics and Data Science, Cornell University, Ithaca, NY"],"affiliations":[{"raw_affiliation_string":"Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu, Taiwan; Department of Statistics and Data Science, Cornell University, Ithaca, NY","institution_ids":["https://openalex.org/I148366613","https://openalex.org/I205783295"]},{"raw_affiliation_string":"Department of Statistics and Data Science, Cornell University, Ithaca, NY","institution_ids":["https://openalex.org/I205783295"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5052673069"],"corresponding_institution_ids":["https://openalex.org/I148366613","https://openalex.org/I205783295"],"apc_list":null,"apc_paid":null,"fwci":0.6704,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.72588437,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"33","issue":"2","first_page":"435","last_page":"447"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9993000030517578,"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":0.9993000030517578,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9962000250816345,"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/T10136","display_name":"Statistical Methods and 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/computer-science","display_name":"Computer science","score":0.7247597575187683},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5786681771278381},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.5776922106742859},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.5249507427215576},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.522125780582428},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.5133863687515259},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.482130765914917},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.47194594144821167},{"id":"https://openalex.org/keywords/model-selection","display_name":"Model selection","score":0.4582379162311554},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4481913447380066},{"id":"https://openalex.org/keywords/sample-size-determination","display_name":"Sample size determination","score":0.4201815724372864},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3906547427177429},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1385587453842163},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1265755295753479}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7247597575187683},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5786681771278381},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.5776922106742859},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.5249507427215576},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.522125780582428},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.5133863687515259},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.482130765914917},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.47194594144821167},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.4582379162311554},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4481913447380066},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.4201815724372864},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3906547427177429},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1385587453842163},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1265755295753479},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/10618600.2023.2260052","is_oa":false,"landing_page_url":"https://doi.org/10.1080/10618600.2023.2260052","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":[{"id":"https://openalex.org/G1557141267","display_name":null,"funder_award_id":"DMS-18102925","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G2520205449","display_name":null,"funder_award_id":"#1934745","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3398401999","display_name":null,"funder_award_id":"111-2634-F-A49-014","funder_id":"https://openalex.org/F2461203286","funder_display_name":"National Science and Technology Council"},{"id":"https://openalex.org/G8697452720","display_name":null,"funder_award_id":"110-2118-M-A49-002-MY3","funder_id":"https://openalex.org/F2461203286","funder_display_name":"National Science and Technology Council"}],"funders":[{"id":"https://openalex.org/F2461203286","display_name":"National Science and Technology Council","ror":"https://ror.org/02kv4zf79"},{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1495929194","https://openalex.org/W2003657827","https://openalex.org/W2035729201","https://openalex.org/W2061320421","https://openalex.org/W2082357899","https://openalex.org/W2094514589","https://openalex.org/W2125275885","https://openalex.org/W2128728535","https://openalex.org/W2166163519","https://openalex.org/W2766451779","https://openalex.org/W3032084519","https://openalex.org/W3043888703","https://openalex.org/W3099924168","https://openalex.org/W3156809662","https://openalex.org/W3204212820","https://openalex.org/W4206341000","https://openalex.org/W4213099427","https://openalex.org/W4249303080"],"related_works":["https://openalex.org/W4390608645","https://openalex.org/W4247566972","https://openalex.org/W4394895745","https://openalex.org/W2960264696","https://openalex.org/W3090563135","https://openalex.org/W2497432351","https://openalex.org/W4206777497","https://openalex.org/W2910064364","https://openalex.org/W4255224757","https://openalex.org/W2499527417"],"abstract_inverted_index":{"It":[0,176],"is":[1,20,34,56,116,131,177],"difficult":[2],"to":[3,28,35,57,87,125],"handle":[4],"the":[5,59,66,78,113,138,172,190],"extraordinary":[6],"data":[7,38,55],"volume":[8],"generated":[9],"in":[10,73],"many":[11],"fields":[12],"with":[13],"current":[14],"computational":[15],"resources":[16],"and":[17,48,102,153,188],"techniques.":[18],"This":[19],"very":[21],"challenging":[22],"when":[23],"applying":[24],"conventional":[25],"statistical":[26],"methods":[27],"big":[29,174],"data.":[30,61,80,140,175],"A":[31],"common":[32],"approach":[33],"partition":[36],"full":[37,60,79,139],"into":[39],"smaller":[40],"subdata":[41,70,94,107,121,166],"for":[42,149,195],"purposes":[43],"such":[44,98,120],"as":[45,92,99],"training,":[46],"testing,":[47],"validation.":[49],"The":[50,164],"primary":[51],"purpose":[52],"of":[53,68,77,137,158,171],"training":[54,69,93,162],"represent":[58],"To":[62],"achieve":[63],"this":[64,142,196],"goal,":[65],"selection":[67],"becomes":[71],"pivotal":[72],"retaining":[74],"essential":[75],"characteristics":[76,170],"Recently,":[81],"several":[82],"procedures":[83],"have":[84],"been":[85],"proposed":[86,165],"select":[88,189],"\"optimal":[89],"design":[90],"points\"":[91],"under":[95],"pre-specified":[96],"models,":[97],"linear":[100],"regression":[101],"logistic":[103],"regression.":[104],"However,":[105],"these":[106],"will":[108],"not":[109,117,132],"be":[110,123],"\"optimal\"":[111],"if":[112],"assumed":[114],"model":[115,151,187],"appropriate.":[118],"Furthermore,":[119],"cannot":[122],"useful":[124],"build":[126],"alternative":[127],"models":[128],"because":[129],"it":[130],"an":[133],"appropriate":[134],"representative":[135],"sample":[136],"In":[141],"article,":[143],"we":[144],"propose":[145],"a":[146,156,160],"novel":[147],"algorithm":[148],"better":[150],"building":[152],"prediction":[154],"via":[155],"process":[157],"selecting":[159],"\"good\"":[161],"sample.":[163],"can":[167,183],"retain":[168],"most":[169],"original":[173],"also":[178],"more":[179],"robust":[180],"that":[181],"one":[182],"fit":[184],"various":[185],"response":[186],"optimal":[191],"model.":[192],"Supplementary":[193],"materials":[194],"article":[197],"are":[198],"available":[199],"online.":[200]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-10T00:00:00"}