{"id":"https://openalex.org/W2563339771","doi":"https://doi.org/10.1080/00401706.2016.1272495","title":"Tensor Envelope Partial Least-Squares Regression","display_name":"Tensor Envelope Partial Least-Squares Regression","publication_year":2016,"publication_date":"2016-12-20","ids":{"openalex":"https://openalex.org/W2563339771","doi":"https://doi.org/10.1080/00401706.2016.1272495","mag":"2563339771"},"language":"en","primary_location":{"id":"doi:10.1080/00401706.2016.1272495","doi":"https://doi.org/10.1080/00401706.2016.1272495","is_oa":false,"landing_page_url":"https://doi.org/10.1080/00401706.2016.1272495","pdf_url":null,"source":{"id":"https://openalex.org/S985303","display_name":"Technometrics","issn_l":"0040-1706","issn":["0040-1706","1537-2723"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["abdc-a","anvur-class-a","anvur-scientific","cwts-core","dongbi-c","doyens","jufo-2","norway-2","poland-140","russia-white-list-1","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":"Technometrics","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/Tensor_Envelope_Partial_Least_Squares_Regression/4483862","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100327543","display_name":"Xin Zhang","orcid":"https://orcid.org/0000-0002-9433-3572","observed_orcids":["https://orcid.org/0000-0002-9433-3572"]},"institutions":[{"id":"https://openalex.org/I103163165","display_name":"Florida State University","ror":"https://ror.org/05g3dte14","country_code":"US","type":"education","lineage":["https://openalex.org/I103163165"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xin Zhang","raw_affiliation_strings":["Department of Statistics, Florida State University, Tallahassee, FL"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, Florida State University, Tallahassee, FL","institution_ids":["https://openalex.org/I103163165"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017399049","display_name":"Lexin Li","orcid":"https://orcid.org/0000-0003-2962-1989","observed_orcids":["https://orcid.org/0000-0003-2962-1989"]},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Lexin Li","raw_affiliation_strings":["Division of Biostatistics, University of California, Berkeley, CA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Division of Biostatistics, University of California, Berkeley, CA","institution_ids":["https://openalex.org/I95457486"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5017399049"],"corresponding_institution_ids":["https://openalex.org/I95457486"],"apc_list":null,"apc_paid":null,"fwci":1.4315,"has_fulltext":false,"cited_by_count":69,"citation_normalized_percentile":{"value":0.81021898,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"59","issue":"4","first_page":"426","last_page":"436"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"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/T12303","display_name":"Tensor decomposition and applications","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"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/T11871","display_name":"Advanced Statistical Methods and Models","score":0.0008999999845400453,"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":0.00019999999494757503,"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/tensor-data","display_name":"tensor data","score":0.6240000128746033},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"dimensionality reduction","score":0.9049999713897705},{"id":"https://openalex.org/keywords/high-dimensional-regression","display_name":"high-dimensional regression","score":0.8270000219345093},{"id":"https://openalex.org/keywords/population-models","display_name":"population models","score":0.652999997138977},{"id":"https://openalex.org/keywords/sufficient-dimension-reduction","display_name":"sufficient dimension reduction","score":0.8460000157356262},{"id":"https://openalex.org/keywords/asymptotic-consistency","display_name":"asymptotic consistency","score":0.996999979019165},{"id":"https://openalex.org/keywords/neuroimaging-data","display_name":"neuroimaging data","score":0.8799999952316284}],"concepts":[{"id":"https://openalex.org/C22354355","wikidata":"https://www.wikidata.org/wiki/Q422009","display_name":"Partial least squares regression","level":2,"score":0.7700271606445312},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.6475560069084167},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6402950286865234},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.569575846195221},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.5477898716926575},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.5380762219429016},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.5288186073303223},{"id":"https://openalex.org/C527412718","wikidata":"https://www.wikidata.org/wiki/Q855395","display_name":"Interpretation (philosophy)","level":2,"score":0.5230908393859863},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5129908919334412},{"id":"https://openalex.org/C9936470","wikidata":"https://www.wikidata.org/wiki/Q6510405","display_name":"Least-squares function approximation","level":3,"score":0.47733646631240845},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.47061899304389954},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3757704794406891},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.35012078285217285},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3361075818538666},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3283422291278839},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.27498120069503784},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.1080/00401706.2016.1272495","doi":"https://doi.org/10.1080/00401706.2016.1272495","is_oa":false,"landing_page_url":"https://doi.org/10.1080/00401706.2016.1272495","pdf_url":null,"source":{"id":"https://openalex.org/S985303","display_name":"Technometrics","issn_l":"0040-1706","issn":["0040-1706","1537-2723"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["abdc-a","anvur-class-a","anvur-scientific","cwts-core","dongbi-c","doyens","jufo-2","norway-2","poland-140","russia-white-list-1","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":"Technometrics","raw_type":"journal-article"},{"id":"pmh:oai:figshare.com:article/4483862","doi":null,"is_oa":true,"landing_page_url":"https://figshare.com/articles/journal_contribution/Tensor_Envelope_Partial_Least_Squares_Regression/4483862","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.4483862","doi":"https://doi.org/10.6084/m9.figshare.4483862","is_oa":true,"landing_page_url":"https://doi.org/10.6084/m9.figshare.4483862","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"},{"id":"doi:10.6084/m9.figshare.4483862.v1","doi":"https://doi.org/10.6084/m9.figshare.4483862.v1","is_oa":true,"landing_page_url":"https://doi.org/10.6084/m9.figshare.4483862.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"},{"id":"doi:10.6084/m9.figshare.4483862.v2","doi":"https://doi.org/10.6084/m9.figshare.4483862.v2","is_oa":true,"landing_page_url":"https://doi.org/10.6084/m9.figshare.4483862.v2","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/4483862","doi":null,"is_oa":true,"landing_page_url":"https://figshare.com/articles/journal_contribution/Tensor_Envelope_Partial_Least_Squares_Regression/4483862","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":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":46,"referenced_works":["https://openalex.org/W242257288","https://openalex.org/W1532814032","https://openalex.org/W1809557031","https://openalex.org/W1967696752","https://openalex.org/W1969905324","https://openalex.org/W1972689360","https://openalex.org/W1973485376","https://openalex.org/W1976251851","https://openalex.org/W1980067858","https://openalex.org/W2002448960","https://openalex.org/W2002820708","https://openalex.org/W2002978455","https://openalex.org/W2010134418","https://openalex.org/W2010498655","https://openalex.org/W2011756661","https://openalex.org/W2019038711","https://openalex.org/W2024165284","https://openalex.org/W2030434042","https://openalex.org/W2044788288","https://openalex.org/W2046605455","https://openalex.org/W2052211772","https://openalex.org/W2057846413","https://openalex.org/W2079775628","https://openalex.org/W2081508613","https://openalex.org/W2086014844","https://openalex.org/W2097057782","https://openalex.org/W2104798373","https://openalex.org/W2110510087","https://openalex.org/W2113506774","https://openalex.org/W2123553417","https://openalex.org/W2136002544","https://openalex.org/W2155020791","https://openalex.org/W2155639125","https://openalex.org/W2158098176","https://openalex.org/W2160333357","https://openalex.org/W2164001739","https://openalex.org/W2164583936","https://openalex.org/W2165093044","https://openalex.org/W2188880327","https://openalex.org/W2327770634","https://openalex.org/W2409256144","https://openalex.org/W2471600425","https://openalex.org/W3101958082","https://openalex.org/W4213170682","https://openalex.org/W4249750499","https://openalex.org/W4252602300"],"related_works":["https://openalex.org/W4401587172","https://openalex.org/W7082873506","https://openalex.org/W7152501186","https://openalex.org/W7117254408","https://openalex.org/W4250378304","https://openalex.org/W32887270","https://openalex.org/W6980004548","https://openalex.org/W1781556363","https://openalex.org/W2604999944","https://openalex.org/W4404020245"],"abstract_inverted_index":{"Partial":[0],"least":[1],"squares":[2],"(PLS)":[3],"is":[4],"a":[5,33,53],"prominent":[6],"solution":[7],"for":[8,132],"dimension":[9,80,90],"reduction":[10],"and":[11,36,38,92,108,125],"high-dimensional":[12],"regressions.":[13],"Recent":[14],"prevalence":[15],"of":[16,26,41,88,98,119],"multidimensional":[17],"tensor":[18,24,55],"data":[19,128],"has":[20],"led":[21],"to":[22,70,82,93],"several":[23],"versions":[25],"the":[27,42,61,76,86,95,99,117,120],"PLS":[28,77,100],"algorithms.":[29],"However,":[30],"none":[31],"offers":[32],"population":[34,63,66],"model":[35],"interpretation,":[37],"statistical":[39],"properties":[40],"associated":[43],"parameters":[44],"remain":[45],"intractable.":[46],"In":[47],"this":[48,133],"article,":[49],"we":[50],"first":[51],"propose":[52],"new":[54,72,121],"partial":[56],"least-squares":[57],"algorithm,":[58],"then":[59],"establish":[60],"corresponding":[62],"interpretation.":[64],"This":[65],"investigation":[67],"allows":[68],"us":[69],"gain":[71],"insight":[73],"on":[74,123],"how":[75],"achieves":[78],"effective":[79],"reduction,":[81,91],"build":[83],"connection":[84],"with":[85,110],"notion":[87],"sufficient":[89],"obtain":[94],"asymptotic":[96],"consistency":[97],"estimator.":[101],"We":[102,114],"compare":[103],"our":[104],"method,":[105],"both":[106],"analytically":[107],"numerically,":[109],"some":[111],"alternative":[112],"solutions.":[113],"also":[115],"illustrate":[116],"efficacy":[118],"method":[122],"simulations":[124],"two":[126],"neuroimaging":[127],"analyses.":[129],"Supplementary":[130],"materials":[131],"article":[134],"are":[135],"available":[136],"online.":[137]},"counts_by_year":[{"year":2026,"cited_by_count":9},{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":11},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":3}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2017-01-06T00:00:00"}