{"id":"https://openalex.org/W1747316516","doi":"https://doi.org/10.1109/iscas.2003.1205823","title":"On unbiased parameter estimation of autoregressive signals observed in noise","display_name":"On unbiased parameter estimation of autoregressive signals observed in noise","publication_year":2003,"publication_date":"2003-11-04","ids":{"openalex":"https://openalex.org/W1747316516","doi":"https://doi.org/10.1109/iscas.2003.1205823","mag":"1747316516"},"language":"en","primary_location":{"id":"doi:10.1109/iscas.2003.1205823","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscas.2003.1205823","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2003 International Symposium on Circuits and Systems, 2003. ISCAS '03.","raw_type":"proceedings-article"},"type":"conference-paper","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/A5108050907","display_name":"Wei Xing Zheng","orcid":"https://orcid.org/0000-0002-0572-5938"},"institutions":[{"id":"https://openalex.org/I63525965","display_name":"Western Sydney University","ror":"https://ror.org/03t52dk35","country_code":"AU","type":"education","lineage":["https://openalex.org/I63525965"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Wei Xing Zheng","raw_affiliation_strings":["School of QMMS, University of Western Ontario, Penrith South, NSW, Australia","Sch. of QMMS, Univ. of Western Sydney, Penrith South, NSW, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of QMMS, University of Western Ontario, Penrith South, NSW, Australia","institution_ids":["https://openalex.org/I63525965"]},{"raw_affiliation_string":"Sch. of QMMS, Univ. of Western Sydney, Penrith South, NSW, Australia","institution_ids":["https://openalex.org/I63525965"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5108050907"],"corresponding_institution_ids":["https://openalex.org/I63525965"],"apc_list":null,"apc_paid":null,"fwci":0.3179,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.46656274,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"4","issue":null,"first_page":"IV","last_page":"261"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9987000226974487,"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"}},"topics":[{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9987000226974487,"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/T10931","display_name":"Direction-of-Arrival Estimation Techniques","score":0.9954000115394592,"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/T10534","display_name":"Structural Health Monitoring Techniques","score":0.9947999715805054,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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":0.8848726749420166},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.6511711478233337},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.6416129469871521},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5875613689422607},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5872907042503357},{"id":"https://openalex.org/keywords/estimation-theory","display_name":"Estimation theory","score":0.5184032917022705},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.5147028565406799},{"id":"https://openalex.org/keywords/signal-to-noise-ratio","display_name":"Signal-to-noise ratio (imaging)","score":0.47101911902427673},{"id":"https://openalex.org/keywords/noise-measurement","display_name":"Noise measurement","score":0.46802717447280884},{"id":"https://openalex.org/keywords/least-squares-function-approximation","display_name":"Least-squares function approximation","score":0.46470680832862854},{"id":"https://openalex.org/keywords/noise-power","display_name":"Noise power","score":0.43917977809906006},{"id":"https://openalex.org/keywords/simple","display_name":"Simple (philosophy)","score":0.42630085349082947},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.31827425956726074},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3004605174064636},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.29314595460891724},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.20895594358444214},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.16964244842529297},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.05903482437133789}],"concepts":[{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.8848726749420166},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.6511711478233337},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.6416129469871521},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5875613689422607},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5872907042503357},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.5184032917022705},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.5147028565406799},{"id":"https://openalex.org/C13944312","wikidata":"https://www.wikidata.org/wiki/Q7512748","display_name":"Signal-to-noise ratio (imaging)","level":2,"score":0.47101911902427673},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.46802717447280884},{"id":"https://openalex.org/C9936470","wikidata":"https://www.wikidata.org/wiki/Q6510405","display_name":"Least-squares function approximation","level":3,"score":0.46470680832862854},{"id":"https://openalex.org/C203234222","wikidata":"https://www.wikidata.org/wiki/Q2133519","display_name":"Noise power","level":3,"score":0.43917977809906006},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.42630085349082947},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.31827425956726074},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3004605174064636},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.29314595460891724},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.20895594358444214},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.16964244842529297},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.05903482437133789},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","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},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iscas.2003.1205823","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscas.2003.1205823","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2003 International Symposium on Circuits and Systems, 2003. ISCAS '03.","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.6899999976158142,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W2086635086","https://openalex.org/W2102246454","https://openalex.org/W2107896723","https://openalex.org/W2122505973","https://openalex.org/W2132384444","https://openalex.org/W2144604702","https://openalex.org/W4309573086","https://openalex.org/W6681835656"],"related_works":["https://openalex.org/W2131248431","https://openalex.org/W1952484348","https://openalex.org/W2097897855","https://openalex.org/W2151952539","https://openalex.org/W2131193330","https://openalex.org/W2411053396","https://openalex.org/W2330428352","https://openalex.org/W2620676623","https://openalex.org/W1505099478","https://openalex.org/W2104729299"],"abstract_inverted_index":{"In":[0,41],"a":[1,4,52],"recent":[2],"paper,":[3,44],"simple":[5],"least-squares":[6],"(LS)":[7],"based":[8,47],"algorithm":[9,48,64,96],"is":[10,39,49,65,85],"introduced":[11],"for":[12,82,97],"unbiased":[13],"parameter":[14],"estimation":[15,95],"of":[16,61,70,74,92],"autoregressive":[17],"(AR)":[18],"signals":[19],"observed":[20],"in":[21],"noise,":[22],"under":[23],"the":[24,27,30,35,42,62,72,75,90],"assumption":[25,73],"that":[26],"ratio":[28,78],"between":[29],"driving":[31],"source":[32],"power":[33,77],"and":[34],"corrupting":[36],"noise":[37],"variance":[38],"known.":[40],"present":[43],"this":[45],"LS":[46],"modified":[50,63],"with":[51],"more":[53],"computationally":[54],"efficient":[55],"algorithmic":[56],"structure.":[57],"The":[58,68],"mean":[59],"convergence":[60],"then":[66],"investigated.":[67],"issue":[69],"how":[71],"known":[76],"can":[79],"be":[80],"mitigated":[81],"practical":[83],"applications":[84],"discussed,":[86],"which":[87],"leads":[88],"to":[89],"development":[91],"an":[93],"effective":[94],"noisy":[98],"AR":[99],"signals.":[100],"Theoretical":[101],"results":[102],"are":[103],"validated":[104],"through":[105],"computer":[106],"simulations.":[107]},"counts_by_year":[{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}