{"id":"https://openalex.org/W4366140274","doi":"https://doi.org/10.1080/00401706.2023.2203184","title":"A General Modeling Framework for Network Autoregressive Processes","display_name":"A General Modeling Framework for Network Autoregressive Processes","publication_year":2023,"publication_date":"2023-04-17","ids":{"openalex":"https://openalex.org/W4366140274","doi":"https://doi.org/10.1080/00401706.2023.2203184"},"language":"en","primary_location":{"id":"doi:10.1080/00401706.2023.2203184","doi":"https://doi.org/10.1080/00401706.2023.2203184","is_oa":false,"landing_page_url":"https://doi.org/10.1080/00401706.2023.2203184","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","is_traditional":true,"indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2110.09596","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5005923675","display_name":"Hang Yin","orcid":"https://orcid.org/0009-0004-1794-593X","observed_orcids":["https://orcid.org/0009-0004-1794-593X","https://orcid.org/0000-0002-7930-0027"]},"institutions":[{"id":"https://openalex.org/I33213144","display_name":"University of Florida","ror":"https://ror.org/02y3ad647","country_code":"US","type":"education","lineage":["https://openalex.org/I33213144"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hang Yin","raw_affiliation_strings":["Department of Statistics, University of Florida, Gainesville, FL;"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, University of Florida, Gainesville, FL;","institution_ids":["https://openalex.org/I33213144"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007282531","display_name":"Abolfazl Safikhani","orcid":"https://orcid.org/0000-0001-8678-1247","observed_orcids":["https://orcid.org/0000-0001-8678-1247"]},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"education","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Abolfazl Safikhani","raw_affiliation_strings":["Department of Statistics, George Mason University, Fairfax, VA;"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, George Mason University, Fairfax, VA;","institution_ids":["https://openalex.org/I162714631"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017220650","display_name":"George C. Michailidis","orcid":"https://orcid.org/0000-0002-3676-1739","observed_orcids":["https://orcid.org/0000-0002-3676-1739"]},"institutions":[{"id":"https://openalex.org/I33213144","display_name":"University of Florida","ror":"https://ror.org/02y3ad647","country_code":"US","type":"education","lineage":["https://openalex.org/I33213144"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"George Michailidis","raw_affiliation_strings":["Department of Statistics & Informatics Institute, University of Florida, Gainesville, FL"],"raw_orcid":"https://orcid.org/0000-0002-3676-1739","affiliations":[{"raw_affiliation_string":"Department of Statistics & Informatics Institute, University of Florida, Gainesville, FL","institution_ids":["https://openalex.org/I33213144"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5017220650"],"corresponding_institution_ids":["https://openalex.org/I33213144"],"apc_list":null,"apc_paid":null,"fwci":3.129,"has_fulltext":true,"cited_by_count":18,"citation_normalized_percentile":{"value":0.91803671,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":88,"max":99},"biblio":{"volume":"65","issue":"4","first_page":"579","last_page":"589"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11911","display_name":"Spatial and Panel Data Analysis","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11911","display_name":"Spatial and Panel Data Analysis","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":9.999999747378752e-05,"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/T11871","display_name":"Advanced Statistical Methods and Models","score":0.0,"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/spatial-autoregressive-model","display_name":"spatial autoregressive model","score":0.7160000205039978},{"id":"https://openalex.org/keywords/heavy-tailed-errors","display_name":"heavy-tailed errors","score":0.8019999861717224},{"id":"https://openalex.org/keywords/stationarity-conditions","display_name":"stationarity conditions","score":0.8740000128746033},{"id":"https://openalex.org/keywords/generalized-least-squares","display_name":"generalized least squares","score":0.7900000214576721},{"id":"https://openalex.org/keywords/ridge-regression","display_name":"ridge regression","score":0.8730000257492065},{"id":"https://openalex.org/keywords/asymptotic-distribution","display_name":"asymptotic distribution","score":0.921999990940094},{"id":"https://openalex.org/keywords/air-pollution-data","display_name":"air pollution data","score":0.5809999704360962},{"id":"https://openalex.org/keywords/large-scale-networks","display_name":"large-scale networks","score":0.39800000190734863}],"concepts":[{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.7992522716522217},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6952873468399048},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.6512448191642761},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.565191388130188},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.5566278696060181},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5284621119499207},{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.5074455142021179},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.5048392415046692},{"id":"https://openalex.org/C194657046","wikidata":"https://www.wikidata.org/wiki/Q7394685","display_name":"STAR model","level":4,"score":0.42096638679504395},{"id":"https://openalex.org/C99656134","wikidata":"https://www.wikidata.org/wiki/Q2912993","display_name":"Ordinary least squares","level":2,"score":0.4119284451007843},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.363834947347641},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.359866201877594},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.31028300523757935},{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.3056904673576355},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.11199316382408142},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.1080/00401706.2023.2203184","doi":"https://doi.org/10.1080/00401706.2023.2203184","is_oa":false,"landing_page_url":"https://doi.org/10.1080/00401706.2023.2203184","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:arXiv.org:2110.09596","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2110.09596","pdf_url":"https://arxiv.org/pdf/2110.09596","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":null,"raw_type":"text"},{"id":"pmh:oai:figshare.com:article/22647337","doi":null,"is_oa":true,"landing_page_url":"https://figshare.com/articles/dataset/A_General_Modeling_Framework_for_Network_Autoregressive_Processes/22647337","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":"Dataset"},{"id":"doi:10.48550/arxiv.2110.09596","doi":"https://doi.org/10.48550/arxiv.2110.09596","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2110.09596","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":false,"raw_source_name":null,"raw_type":"Preprint"},{"id":"doi:10.6084/m9.figshare.22647337.v1","doi":"https://doi.org/10.6084/m9.figshare.22647337.v1","is_oa":true,"landing_page_url":"https://doi.org/10.6084/m9.figshare.22647337.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":"Dataset"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2110.09596","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2110.09596","pdf_url":"https://arxiv.org/pdf/2110.09596","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4366140274.pdf","grobid_xml":"https://content.openalex.org/works/W4366140274.grobid-xml"},"referenced_works_count":21,"referenced_works":["https://openalex.org/W1986866669","https://openalex.org/W1990381576","https://openalex.org/W2000601868","https://openalex.org/W2014859977","https://openalex.org/W2049287603","https://openalex.org/W2071673199","https://openalex.org/W2094514178","https://openalex.org/W2110092447","https://openalex.org/W2145758752","https://openalex.org/W2170202041","https://openalex.org/W2739653867","https://openalex.org/W2746759718","https://openalex.org/W2947319531","https://openalex.org/W2947626232","https://openalex.org/W2975967059","https://openalex.org/W3040199983","https://openalex.org/W3041392591","https://openalex.org/W3124251522","https://openalex.org/W3146166473","https://openalex.org/W3184825522","https://openalex.org/W4390012468"],"related_works":["https://openalex.org/W3124251522","https://openalex.org/W4392830725","https://openalex.org/W2994839621","https://openalex.org/W2975967059","https://openalex.org/W3155925589","https://openalex.org/W4406306876","https://openalex.org/W4213421061","https://openalex.org/W4390723244","https://openalex.org/W4395483127","https://openalex.org/W3041392591"],"abstract_inverted_index":{"A":[0],"general":[1,60],"flexible":[2],"framework":[3,46,173],"for":[4,100,140],"Network":[5],"Autoregressive":[6],"Processes":[7],"(NAR)":[8],"is":[9,78,174],"developed,":[10],"wherein":[11],"the":[12,18,45,70,74,88,151,155,162,167],"response":[13],"of":[14,30,36,73,108,144,153,166],"each":[15],"node":[16],"in":[17,57,84,87,122],"network":[19,109,124,156],"linearly":[20],"depends":[21],"on":[22,161,176],"its":[23,82,159],"past":[24],"values,":[25],"a":[26,34,65],"prespecified":[27],"linear":[28],"combination":[29],"neighboring":[31],"nodes":[32],"and":[33,44,94,111,158,179],"set":[35],"node-specific":[37],"covariates.":[38],"The":[39,172],"corresponding":[40],"coefficients":[41],"are":[42],"node-specific,":[43],"can":[47,137],"accommodate":[48],"heavier":[49],"than":[50,81],"Gaussian":[51],"errors":[52],"with":[53,127],"spatial-autoregressive,":[54],"factor-based,":[55],"or":[56],"certain":[58],"settings":[59],"covariance":[61],"structures.":[62],"We":[63,131,148],"provide":[64,113],"sufficient":[66],"condition":[67],"that":[68,77,118,136],"ensures":[69],"stability":[71],"(stationarity)":[72],"underlying":[75],"NAR":[76,169],"significantly":[79],"weaker":[80],"counterparts":[83,117],"previous":[85],"work":[86],"literature.":[89],"Further,":[90],"we":[91],"develop":[92],"ordinary":[93],"(estimated)":[95],"generalized":[96],"least":[97],"squares":[98],"estimators":[99],"both":[101,177],"fixed,":[102],"as":[103,105],"well":[104],"diverging":[106],"numbers":[107],"nodes,":[110],"also":[112,149],"their":[114,128,133],"ridge":[115],"regularized":[116],"exhibit":[119],"better":[120],"performance":[121],"large":[123],"settings,":[125],"together":[126],"asymptotic":[129,134,164],"distributions.":[130],"derive":[132],"distributions":[135,165],"be":[138],"used":[139],"testing":[141],"various":[142,168],"hypotheses":[143],"interest":[145],"to":[146],"practitioners.":[147],"address":[150],"issue":[152],"misspecifying":[154],"connectivity":[157],"impact":[160],"aforementioned":[163],"parameter":[170],"estimators.":[171],"illustrated":[175],"synthetic":[178],"real":[180],"air":[181],"pollution":[182],"data.":[183]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":1}],"updated_date":"2026-11-10T11:39:43.021295","created_date":"2023-04-19T00:00:00"}
