{"id":"https://openalex.org/W2115874690","doi":"https://doi.org/10.1109/icassp.1984.1172320","title":"Performance comparison of autoregressive estimation methods","display_name":"Performance comparison of autoregressive estimation methods","publication_year":2005,"publication_date":"2005-03-24","ids":{"openalex":"https://openalex.org/W2115874690","doi":"https://doi.org/10.1109/icassp.1984.1172320","mag":"2115874690"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.1984.1172320","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.1984.1172320","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP '84. IEEE International Conference on Acoustics, Speech, and Signal Processing","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/A5055162882","display_name":"Shaoann Shon","orcid":null},"institutions":[{"id":"https://openalex.org/I4210122954","display_name":"MIT Lincoln Laboratory","ror":"https://ror.org/022z6jk58","country_code":"US","type":"facility","lineage":["https://openalex.org/I4210122954","https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shaoann Shon","raw_affiliation_strings":["M. I. T. Lincoln Laboratory, Lexington, MA, USA","MIT/Lincoln Laboratory, Lexington, MA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"M. I. T. Lincoln Laboratory, Lexington, MA, USA","institution_ids":["https://openalex.org/I4210122954"]},{"raw_affiliation_string":"MIT/Lincoln Laboratory, Lexington, MA","institution_ids":["https://openalex.org/I4210122954"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112800901","display_name":"K. Mehrotra","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"K. Mehrotra","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.6191,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.65657635,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"9","issue":null,"first_page":"581","last_page":"584"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11236","display_name":"Control Systems and Identification","score":0.9901000261306763,"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"}},"topics":[{"id":"https://openalex.org/T11236","display_name":"Control Systems and Identification","score":0.9901000261306763,"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"}},{"id":"https://openalex.org/T10534","display_name":"Structural Health Monitoring Techniques","score":0.9879999756813049,"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"}},{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9833999872207642,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.9370748996734619},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.8033509254455566},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5928056240081787},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5093046426773071},{"id":"https://openalex.org/keywords/star-model","display_name":"STAR model","score":0.4981076717376709},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.49542176723480225},{"id":"https://openalex.org/keywords/spectral-density-estimation","display_name":"Spectral density estimation","score":0.47934040427207947},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.47088131308555603},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.4410952627658844},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4344395101070404},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4255026578903198},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.41008660197257996},{"id":"https://openalex.org/keywords/autoregressive-integrated-moving-average","display_name":"Autoregressive integrated moving average","score":0.21280372142791748},{"id":"https://openalex.org/keywords/fourier-transform","display_name":"Fourier transform","score":0.141910582780838}],"concepts":[{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.9370748996734619},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.8033509254455566},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5928056240081787},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5093046426773071},{"id":"https://openalex.org/C194657046","wikidata":"https://www.wikidata.org/wiki/Q7394685","display_name":"STAR model","level":4,"score":0.4981076717376709},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.49542176723480225},{"id":"https://openalex.org/C30049272","wikidata":"https://www.wikidata.org/wiki/Q6555326","display_name":"Spectral density estimation","level":3,"score":0.47934040427207947},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.47088131308555603},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.4410952627658844},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4344395101070404},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4255026578903198},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.41008660197257996},{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.21280372142791748},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"score":0.141910582780838},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.1984.1172320","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.1984.1172320","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP '84. IEEE International Conference on Acoustics, Speech, and Signal Processing","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":5,"referenced_works":["https://openalex.org/W2054278438","https://openalex.org/W2143082178","https://openalex.org/W2162443378","https://openalex.org/W2313953460","https://openalex.org/W3015894552"],"related_works":["https://openalex.org/W2439807930","https://openalex.org/W2009692134","https://openalex.org/W2019155478","https://openalex.org/W1972271943","https://openalex.org/W2024529895","https://openalex.org/W2168175994","https://openalex.org/W2147584239","https://openalex.org/W2349019353","https://openalex.org/W2154965898","https://openalex.org/W1982139997"],"abstract_inverted_index":{"The":[0,24,77],"statistical":[1],"performances":[2,79],"of":[3,11,80,86,101],"six":[4],"methods":[5,25,82],"in":[6,83],"the":[7,12,17,27,33,39,43,59,81,84,97,102],"finite":[8],"sample":[9],"estimation":[10],"autoregressive":[13],"(AR)":[14],"parameters":[15],"and":[16,48],"spectral":[18],"peak":[19],"frequencies":[20],"are":[21,55,75,91,108,110],"comparatively":[22],"studied.":[23],"are:":[26],"Yule":[28],"Walker":[29],"method,":[30,32,38,42,47],"Burg's":[31],"Box":[34],"Jenkins":[35],"least":[36],"square":[37],"forward":[40,44],"error":[41,46],"backward":[45],"Kay's":[49],"RMLE":[50],"method.":[51],"Precise":[52],"performance":[53],"statistics":[54],"obtained":[56],"analytically":[57],"for":[58],"AR(1)":[60],"parameter":[61],"estimators.":[62],"For":[63],"higher":[64],"order":[65],"processes":[66],"similar":[67],"evaluation":[68],"is":[69],"not":[70],"feasible;":[71],"computer":[72],"simulation":[73],"results":[74],"presented.":[76],"relative":[78],"processing":[85],"stationary":[87],"AR":[88,104],"time":[89],"series":[90],"found":[92],"to":[93],"be":[94],"dependent":[95],"on":[96],"characteristic":[98],"root":[99],"positions":[100],"underlying":[103],"processes.":[105],"Methods":[106],"that":[107],"superior":[109],"identified.":[111]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}