{"id":"https://openalex.org/W2744924819","doi":"https://doi.org/10.1109/isit.2017.8007059","title":"Fundamental estimation limits in autoregressive processes with compressive measurements","display_name":"Fundamental estimation limits in autoregressive processes with compressive measurements","publication_year":2017,"publication_date":"2017-06-01","ids":{"openalex":"https://openalex.org/W2744924819","doi":"https://doi.org/10.1109/isit.2017.8007059","mag":"2744924819"},"language":"en","primary_location":{"id":"doi:10.1109/isit.2017.8007059","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isit.2017.8007059","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Symposium on Information Theory (ISIT)","raw_type":"proceedings-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/A5031236216","display_name":"Milind Rao","orcid":"https://orcid.org/0000-0003-2649-3205"},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Milind Rao","raw_affiliation_strings":["Electrical Engineering, Stanford University, Stanford, CA"],"affiliations":[{"raw_affiliation_string":"Electrical Engineering, Stanford University, Stanford, CA","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059310658","display_name":"Tara Javidi","orcid":"https://orcid.org/0000-0001-7112-1043"},"institutions":[{"id":"https://openalex.org/I36258959","display_name":"University of California, San Diego","ror":"https://ror.org/0168r3w48","country_code":"US","type":"education","lineage":["https://openalex.org/I36258959"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tara Javidi","raw_affiliation_strings":["Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA"],"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA","institution_ids":["https://openalex.org/I36258959"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005913897","display_name":"Yonina C. Eldar","orcid":"https://orcid.org/0000-0003-4358-5304"},"institutions":[{"id":"https://openalex.org/I174306211","display_name":"Technion \u2013 Israel Institute of Technology","ror":"https://ror.org/03qryx823","country_code":"IL","type":"education","lineage":["https://openalex.org/I174306211"]}],"countries":["IL"],"is_corresponding":false,"raw_author_name":"Yonina C. Eldar","raw_affiliation_strings":["Electrical Engineering Technion, Israel Institute of Technology, Haifa, Israel"],"affiliations":[{"raw_affiliation_string":"Electrical Engineering Technion, Israel Institute of Technology, Haifa, Israel","institution_ids":["https://openalex.org/I174306211"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5074116801","display_name":"Andrea Goldsmith","orcid":"https://orcid.org/0000-0001-5686-800X"},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Andrea Goldsmith","raw_affiliation_strings":["Electrical Engineering, Stanford University, Stanford, CA"],"affiliations":[{"raw_affiliation_string":"Electrical Engineering, Stanford University, Stanford, CA","institution_ids":["https://openalex.org/I97018004"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5031236216"],"corresponding_institution_ids":["https://openalex.org/I97018004"],"apc_list":null,"apc_paid":null,"fwci":0.6995,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.74181398,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"2895","last_page":"2899"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9994999766349792,"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/T10136","display_name":"Statistical Methods and Inference","score":0.9994999766349792,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.996999979019165,"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/T11716","display_name":"Random Matrices and Applications","score":0.9932000041007996,"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/autoregressive-model","display_name":"Autoregressive model","score":0.8166170120239258},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.7516360282897949},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.6033248901367188},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.5722300410270691},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.5650505423545837},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.5062793493270874},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5016710758209229},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4720129668712616},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.46743953227996826},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.44790196418762207},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4315926134586334},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.4232475757598877},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.22382158041000366}],"concepts":[{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.8166170120239258},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.7516360282897949},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.6033248901367188},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.5722300410270691},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.5650505423545837},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.5062793493270874},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5016710758209229},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4720129668712616},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.46743953227996826},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.44790196418762207},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4315926134586334},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4232475757598877},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.22382158041000366},{"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/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isit.2017.8007059","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isit.2017.8007059","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Symposium on Information Theory (ISIT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W340244495","https://openalex.org/W1836500409","https://openalex.org/W1875396108","https://openalex.org/W1925915230","https://openalex.org/W2013029130","https://openalex.org/W2019459021","https://openalex.org/W2039244422","https://openalex.org/W2060581589","https://openalex.org/W2072249473","https://openalex.org/W2098056745","https://openalex.org/W2130351130","https://openalex.org/W2154104617","https://openalex.org/W2158849324","https://openalex.org/W2196956961","https://openalex.org/W2586353914","https://openalex.org/W2595614776","https://openalex.org/W2962894999","https://openalex.org/W2963893933","https://openalex.org/W3012485276","https://openalex.org/W3099324314","https://openalex.org/W3099609308","https://openalex.org/W3101788651","https://openalex.org/W3103311997","https://openalex.org/W3105322001","https://openalex.org/W3124642795","https://openalex.org/W3146166473","https://openalex.org/W4241115065","https://openalex.org/W4300263211","https://openalex.org/W4301266338","https://openalex.org/W6638779161","https://openalex.org/W6639232069","https://openalex.org/W6735035532","https://openalex.org/W6766053435"],"related_works":["https://openalex.org/W2158224665","https://openalex.org/W2379589510","https://openalex.org/W2810730439","https://openalex.org/W4300044672","https://openalex.org/W1881631164","https://openalex.org/W2358292267","https://openalex.org/W2171218219","https://openalex.org/W2886934452","https://openalex.org/W1489099099","https://openalex.org/W2024369332"],"abstract_inverted_index":{"We":[0,53,80],"consider":[1],"the":[2,6,19,24,32,38,44,59,65,89,92],"problem":[3],"of":[4,8,18,31,61,76,91],"estimating":[5],"parameters":[7,90],"a":[9],"vector":[10],"autoregressive":[11],"(VAR)":[12],"process":[13,41,94],"from":[14],"low-dimensional":[15],"random":[16],"projections":[17],"observations.":[20],"This":[21],"setting":[22],"covers":[23],"cases":[25],"where":[26],"we":[27],"take":[28],"compressive":[29],"measurements":[30],"observations":[33],"or":[34,67,88],"have":[35],"limits":[36],"in":[37],"data":[39],"acquisition":[40],"associated":[42],"with":[43,70],"measurement":[45],"system":[46],"and":[47,71,78,95],"are":[48],"only":[49],"able":[50],"to":[51],"subsample.":[52],"first":[54],"present":[55],"fundamental":[56],"bounds":[57],"on":[58],"convergence":[60],"any":[62],"estimator":[63,84],"for":[64,85],"covariance":[66],"state-transition":[68],"matrices":[69,87],"without":[72],"considering":[73],"structural":[74],"constraints":[75],"sparsity":[77],"low-rankness.":[79],"then":[81],"construct":[82],"an":[83],"these":[86],"VAR":[93],"show":[96],"that":[97],"it":[98],"is":[99],"order":[100],"optimal.":[101]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}