{"id":"https://openalex.org/W4412158921","doi":"https://doi.org/10.1080/10618600.2025.2528938","title":"Partial Envelope and Reduced-Rank Partial Envelope Vector Autoregressive Models","display_name":"Partial Envelope and Reduced-Rank Partial Envelope Vector Autoregressive Models","publication_year":2025,"publication_date":"2025-07-10","ids":{"openalex":"https://openalex.org/W4412158921","doi":"https://doi.org/10.1080/10618600.2025.2528938"},"language":"en","primary_location":{"id":"doi:10.1080/10618600.2025.2528938","is_oa":false,"landing_page_url":"https://doi.org/10.1080/10618600.2025.2528938","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":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5111325784","display_name":"H. M. Wiranthe B. Herath","orcid":"https://orcid.org/0000-0001-9501-3864"},"institutions":[{"id":"https://openalex.org/I87213936","display_name":"Drake University","ror":"https://ror.org/001skmk61","country_code":"US","type":"education","lineage":["https://openalex.org/I87213936"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"H. M. Wiranthe B. Herath","raw_affiliation_strings":["Zimpleman College of Business, Drake University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zimpleman College of Business, Drake University","institution_ids":["https://openalex.org/I87213936"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054715103","display_name":"S. Yaser Samadi","orcid":"https://orcid.org/0000-0002-6121-0234"},"institutions":[{"id":"https://openalex.org/I1343558604","display_name":"Statistical and Applied Mathematical Sciences Institute","ror":"https://ror.org/01shctp43","country_code":"US","type":"facility","lineage":["https://openalex.org/I1311060795","https://openalex.org/I1343558604"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"S. Yaser Samadi","raw_affiliation_strings":["School of Mathematical and Statistical Sciences, Southern Illinois University"],"raw_orcid":"https://orcid.org/0000-0002-6121-0234","affiliations":[{"raw_affiliation_string":"School of Mathematical and Statistical Sciences, Southern Illinois University","institution_ids":["https://openalex.org/I1343558604"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5054715103"],"corresponding_institution_ids":["https://openalex.org/I1343558604"],"apc_list":null,"apc_paid":null,"fwci":2.3609,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.888569,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"35","issue":"1","first_page":"315","last_page":"329"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11206","display_name":"Model Reduction and Neural Networks","score":0.9714000225067139,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11206","display_name":"Model Reduction and Neural Networks","score":0.9714000225067139,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9700000286102295,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11236","display_name":"Control Systems and Identification","score":0.9074000120162964,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.8288059830665588},{"id":"https://openalex.org/keywords/envelope","display_name":"Envelope (radar)","score":0.7126063108444214},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.5453776717185974},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5439602732658386},{"id":"https://openalex.org/keywords/partial-least-squares-regression","display_name":"Partial least squares regression","score":0.4606582820415497},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.41252970695495605},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.32668980956077576},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.318288117647171},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3131118416786194},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.19392439723014832}],"concepts":[{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.8288059830665588},{"id":"https://openalex.org/C65155139","wikidata":"https://www.wikidata.org/wiki/Q5380912","display_name":"Envelope (radar)","level":3,"score":0.7126063108444214},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.5453776717185974},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5439602732658386},{"id":"https://openalex.org/C22354355","wikidata":"https://www.wikidata.org/wiki/Q422009","display_name":"Partial least squares regression","level":2,"score":0.4606582820415497},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.41252970695495605},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32668980956077576},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.318288117647171},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3131118416786194},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.19392439723014832},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1080/10618600.2025.2528938","is_oa":false,"landing_page_url":"https://doi.org/10.1080/10618600.2025.2528938","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"},{"id":"pmh:doi:10.6084/m9.figshare.29533489","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Dataset"}],"best_oa_location":{"id":"pmh:doi:10.6084/m9.figshare.29533489","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Dataset"},"sustainable_development_goals":[{"score":0.44999998807907104,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320316636","display_name":"Drake University","ror":"https://ror.org/001skmk61"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":58,"referenced_works":["https://openalex.org/W1532814032","https://openalex.org/W1963799935","https://openalex.org/W1969905324","https://openalex.org/W2008573782","https://openalex.org/W2034133761","https://openalex.org/W2037935457","https://openalex.org/W2040825813","https://openalex.org/W2049124732","https://openalex.org/W2050230608","https://openalex.org/W2054121219","https://openalex.org/W2064497778","https://openalex.org/W2076396344","https://openalex.org/W2082220318","https://openalex.org/W2093513340","https://openalex.org/W2096159536","https://openalex.org/W2098588523","https://openalex.org/W2110144299","https://openalex.org/W2110510087","https://openalex.org/W2141202790","https://openalex.org/W2155020791","https://openalex.org/W2164350703","https://openalex.org/W2424333113","https://openalex.org/W2486563685","https://openalex.org/W2586353914","https://openalex.org/W2895284550","https://openalex.org/W2900832355","https://openalex.org/W2907768713","https://openalex.org/W2947626232","https://openalex.org/W2948864351","https://openalex.org/W2963484251","https://openalex.org/W2963716239","https://openalex.org/W2963924474","https://openalex.org/W2964277180","https://openalex.org/W2966068697","https://openalex.org/W2978340534","https://openalex.org/W3004806874","https://openalex.org/W3037431577","https://openalex.org/W3037532055","https://openalex.org/W3107841493","https://openalex.org/W3121553976","https://openalex.org/W3162931473","https://openalex.org/W3211254159","https://openalex.org/W4200476027","https://openalex.org/W4213377538","https://openalex.org/W4230206799","https://openalex.org/W4251038355","https://openalex.org/W4285703539","https://openalex.org/W4291327732","https://openalex.org/W4301375966","https://openalex.org/W4380085580","https://openalex.org/W4386928224","https://openalex.org/W4388357039","https://openalex.org/W4396930874","https://openalex.org/W4399648595","https://openalex.org/W4402511258","https://openalex.org/W4408722866","https://openalex.org/W4410132221","https://openalex.org/W7076010259"],"related_works":["https://openalex.org/W1989457222","https://openalex.org/W2171218219","https://openalex.org/W2158863190","https://openalex.org/W1972271943","https://openalex.org/W2150410159","https://openalex.org/W4327525404","https://openalex.org/W4287185323","https://openalex.org/W1995849744","https://openalex.org/W3147276099","https://openalex.org/W2896232477"],"abstract_inverted_index":{"Traditional":[0],"vector":[1],"autoregressive":[2],"(VAR)":[3],"models":[4,150],"have":[5],"been":[6],"extensively":[7],"used":[8],"in":[9,28],"modeling":[10],"multivariate":[11],"time":[12,80],"series":[13,81],"data":[14,82],"due":[15],"to":[16,106,166,174],"their":[17],"flexibility":[18],"and":[19,36,71,75,126,144,170],"simplicity.":[20],"However,":[21],"they":[22],"often":[23],"suffer":[24],"from":[25],"overparameterization,":[26],"particularly":[27,49],"high-dimensional":[29],"datasets,":[30],"limiting":[31],"the":[32,64,85,117,122,128,138,142],"inclusion":[33],"of":[34,46,78,102,140],"variables":[35,105,160],"lags.":[37,56],"In":[38],"many":[39],"cases,":[40],"specific":[41],"lag":[42,104,159],"effects":[43],"may":[44],"be":[45],"greater":[47],"interest,":[48],"when":[50],"significant":[51,167],"cross-correlations":[52],"occur":[53],"at":[54],"shorter":[55],"To":[57],"address":[58],"these":[59],"challenges,":[60],"we":[61],"first":[62],"propose":[63,127],"partial":[65,118,130,145],"envelope":[66,119,131],"VAR":[67,124,132,147,176],"(PEVAR),":[68],"which":[69],"identifies":[70],"removes":[72],"white":[73],"noise":[74],"immaterial":[76],"components":[77],"complex":[79],"by":[83,115,155],"linking":[84],"mean":[86],"function":[87],"with":[88],"covariance":[89],"structures":[90],"via":[91],"a":[92,100,152],"reduced":[93],"subspace.":[94],"The":[95],"PEVAR":[96,143],"model":[97],"concentrates":[98],"on":[99,157],"subset":[101],"important":[103],"improve":[107],"estimation":[108],"efficiency.":[109],"We":[110],"further":[111],"extend":[112],"this":[113,181],"approach":[114],"integrating":[116],"concept":[120],"into":[121],"reduced-rank":[123,129,146],"framework":[125],"(RPEVAR)":[133],"model.":[134],"RPEVAR":[135],"simultaneously":[136],"integrates":[137],"strengths":[139],"both":[141],"models.":[148,177],"These":[149],"offer":[151],"parsimonious":[153],"technique":[154],"focusing":[156],"relevant":[158],"during":[161],"coefficient":[162],"estimation,":[163],"potentially":[164],"leading":[165],"efficiency":[168],"gains":[169],"enhanced":[171],"accuracy":[172],"relative":[173],"non-partial":[175],"Supplementary":[178],"materials":[179],"for":[180],"article":[182],"are":[183],"available":[184],"online.":[185]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2026-02-21T06:11:54.161237","created_date":"2025-10-10T00:00:00"}
