{"id":"https://openalex.org/W3108992773","doi":"https://doi.org/10.1109/jsait.2020.3041714","title":"Generalized Autoregressive Linear Models for Discrete High-Dimensional Data","display_name":"Generalized Autoregressive Linear Models for Discrete High-Dimensional Data","publication_year":2020,"publication_date":"2020-11-01","ids":{"openalex":"https://openalex.org/W3108992773","doi":"https://doi.org/10.1109/jsait.2020.3041714","mag":"3108992773"},"language":"en","primary_location":{"id":"doi:10.1109/jsait.2020.3041714","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jsait.2020.3041714","pdf_url":null,"source":{"id":"https://openalex.org/S4210211895","display_name":"IEEE Journal on Selected Areas in Information Theory","issn_l":"2641-8770","issn":["2641-8770"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal on Selected Areas in Information Theory","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/A5083224560","display_name":"Parthe Pandit","orcid":"https://orcid.org/0000-0002-2524-8817"},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Parthe Pandit","raw_affiliation_strings":["University of California Los Angeles, Los Angeles, CA, USA"],"affiliations":[{"raw_affiliation_string":"University of California Los Angeles, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I161318765"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112288354","display_name":"Mojtaba Sahraee-Ardakan","orcid":null},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mojtaba Sahraee-Ardakan","raw_affiliation_strings":["University of California Los Angeles, Los Angeles, CA, USA"],"affiliations":[{"raw_affiliation_string":"University of California Los Angeles, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I161318765"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102836790","display_name":"Arash A. Amini","orcid":"https://orcid.org/0000-0002-2808-8310"},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Arash A. Amini","raw_affiliation_strings":["University of California Los Angeles, Los Angeles, CA, USA"],"affiliations":[{"raw_affiliation_string":"University of California Los Angeles, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I161318765"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000099903","display_name":"Sundeep Rangan","orcid":"https://orcid.org/0000-0002-0925-8169"},"institutions":[{"id":"https://openalex.org/I57206974","display_name":"New York University","ror":"https://ror.org/0190ak572","country_code":"US","type":"education","lineage":["https://openalex.org/I57206974"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sundeep Rangan","raw_affiliation_strings":["New York University Tandon School of Engineering, Brooklyn, NY, USA"],"affiliations":[{"raw_affiliation_string":"New York University Tandon School of Engineering, Brooklyn, NY, USA","institution_ids":["https://openalex.org/I57206974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073129946","display_name":"Alyson K. Fletcher","orcid":"https://orcid.org/0000-0002-3756-6580"},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alyson K. Fletcher","raw_affiliation_strings":["University of California Los Angeles, Los Angeles, CA, USA"],"affiliations":[{"raw_affiliation_string":"University of California Los Angeles, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I161318765"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5083224560"],"corresponding_institution_ids":["https://openalex.org/I161318765"],"apc_list":null,"apc_paid":null,"fwci":0.8983,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.70960915,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"1","issue":"3","first_page":"884","last_page":"896"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9951000213623047,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.8069953918457031},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.688627302646637},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5255255103111267},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5250515937805176},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.5220634341239929},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.462752103805542},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4516991972923279},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.4511711299419403},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.44872209429740906},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.44557759165763855},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.4154696464538574},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3873947262763977},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.37329357862472534},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2936568260192871}],"concepts":[{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.8069953918457031},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.688627302646637},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5255255103111267},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5250515937805176},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.5220634341239929},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.462752103805542},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4516991972923279},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.4511711299419403},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.44872209429740906},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.44557759165763855},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.4154696464538574},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3873947262763977},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.37329357862472534},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2936568260192871},{"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/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/jsait.2020.3041714","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jsait.2020.3041714","pdf_url":null,"source":{"id":"https://openalex.org/S4210211895","display_name":"IEEE Journal on Selected Areas in Information Theory","issn_l":"2641-8770","issn":["2641-8770"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal on Selected Areas in Information Theory","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1122607870","display_name":null,"funder_award_id":"1302336","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G1464243006","display_name":null,"funder_award_id":"1254204","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3557183362","display_name":null,"funder_award_id":"N00014-15-1-2677","funder_id":"https://openalex.org/F4320337345","funder_display_name":"Office of Naval Research"},{"id":"https://openalex.org/G4249296430","display_name":null,"funder_award_id":"1738286","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5398790236","display_name":null,"funder_award_id":"1116589","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7667285873","display_name":null,"funder_award_id":"1547332","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320306087","display_name":"Semiconductor Research Corporation","ror":"https://ror.org/047z4n946"},{"id":"https://openalex.org/F4320332178","display_name":"National Institute of Standards and Technology","ror":"https://ror.org/05xpvk416"},{"id":"https://openalex.org/F4320337345","display_name":"Office of Naval Research","ror":"https://ror.org/00rk2pe57"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":60,"referenced_works":["https://openalex.org/W137246141","https://openalex.org/W340244495","https://openalex.org/W1533852475","https://openalex.org/W1542414211","https://openalex.org/W1549664537","https://openalex.org/W1925915230","https://openalex.org/W1972615020","https://openalex.org/W1991266025","https://openalex.org/W1998123258","https://openalex.org/W2009754940","https://openalex.org/W2016851953","https://openalex.org/W2028132509","https://openalex.org/W2029922822","https://openalex.org/W2032291279","https://openalex.org/W2044379973","https://openalex.org/W2102951916","https://openalex.org/W2109843705","https://openalex.org/W2129638195","https://openalex.org/W2132105090","https://openalex.org/W2134130436","https://openalex.org/W2147631685","https://openalex.org/W2159700154","https://openalex.org/W2164452299","https://openalex.org/W2172053542","https://openalex.org/W2188692957","https://openalex.org/W2296616510","https://openalex.org/W2335281168","https://openalex.org/W2520179645","https://openalex.org/W2564963710","https://openalex.org/W2590347053","https://openalex.org/W2785424877","https://openalex.org/W2787248994","https://openalex.org/W2794403111","https://openalex.org/W2794419064","https://openalex.org/W2903470116","https://openalex.org/W2921445555","https://openalex.org/W2947626232","https://openalex.org/W2950190315","https://openalex.org/W2962724596","https://openalex.org/W2962762573","https://openalex.org/W2963396462","https://openalex.org/W2963550386","https://openalex.org/W2963924474","https://openalex.org/W2964204801","https://openalex.org/W3100041486","https://openalex.org/W3102676469","https://openalex.org/W3103870504","https://openalex.org/W3105322001","https://openalex.org/W3105835001","https://openalex.org/W3121553976","https://openalex.org/W3123507112","https://openalex.org/W3141915010","https://openalex.org/W4206196235","https://openalex.org/W4250954493","https://openalex.org/W4250955649","https://openalex.org/W4300263211","https://openalex.org/W4302366875","https://openalex.org/W6632096595","https://openalex.org/W6760839892","https://openalex.org/W6792291861"],"related_works":["https://openalex.org/W2171218219","https://openalex.org/W2150410159","https://openalex.org/W1972271943","https://openalex.org/W3150905897","https://openalex.org/W4327525404","https://openalex.org/W4287185323","https://openalex.org/W1520183331","https://openalex.org/W2099889858","https://openalex.org/W2169866437","https://openalex.org/W1964286703"],"abstract_inverted_index":{"Fitting":[0],"multivariate":[1,26,166],"autoregressive":[2],"(AR)":[3],"models":[4],"is":[5,55,97,113,133,183,192],"fundamental":[6],"for":[7,161],"time-series":[8],"data":[9],"analysis":[10,109],"in":[11,75,105,119,169,185],"a":[12,24,34,44,78,139,151,162],"wide":[13],"range":[14],"of":[15,22,37,64,81,110,126,145,153,159,164,190,199],"applications.":[16],"This":[17],"article":[18],"considers":[19],"the":[20,38,50,58,65,70,108,117,120,124,130,142,146,154,157,170,174,188,196],"problem":[21,54],"learning":[23],"p-lag":[25],"AR":[27,167],"model":[28],"where":[29,187],"each":[30],"time":[31],"step":[32],"involves":[33],"linear":[35,59],"combination":[36],"past":[39],"p":[40],"states":[41],"followed":[42],"by":[43],"probabilistic,":[45],"possibly":[46],"nonlinear,":[47],"mapping":[48],"to":[49,56,100,116],"next":[51],"state.":[52],"The":[53],"learn":[57],"connectivity":[60,148],"tensor":[61,149],"from":[62],"observations":[63],"states.":[66],"We":[67],"focus":[68],"on":[69,141],"sparse":[71],"setting,":[72],"which":[73],"arises":[74],"applications":[76],"with":[77,178],"limited":[79],"number":[80,158,189,198],"direct":[82],"connections":[83],"between":[84],"variables.":[85],"For":[86],"such":[87,111],"problems,":[88],"11-regularized":[89],"maximum":[90],"likelihood":[91],"estimation":[92,182],"(or":[93],"M-estimation":[94],"more":[95],"generally)":[96],"often":[98],"straightforward":[99],"apply":[101],"and":[102,123,156],"works":[103],"well":[104],"practice.":[106],"However,":[107],"methods":[112],"difficult":[114],"due":[115],"feedback":[118],"state":[121],"dynamic":[122],"presence":[125],"nonlinearities,":[127],"especially":[128],"when":[129],"underlying":[131],"process":[132],"non-Gaussian.":[134],"Our":[135],"main":[136],"result":[137],"provides":[138],"bound":[140,175],"mean-squared":[143],"error":[144],"estimated":[147],"as":[150],"function":[152],"sparsity":[155],"samples,":[160],"class":[163],"discrete":[165],"models,":[168],"high-dimensional":[171],"regime.":[172],"Importantly,":[173],"indicates":[176],"that,":[177],"sufficient":[179],"sparsity,":[180],"consistent":[181],"possible":[184],"cases":[186],"samples":[191],"significantly":[193],"less":[194],"than":[195],"total":[197],"parameters.":[200]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-10T00:00:00"}
