{"id":"https://openalex.org/W2089340426","doi":"https://doi.org/10.1109/tsp.2012.2237168","title":"Modeling and Estimation of Covariance of Replicated Modulated Cyclical Time Series","display_name":"Modeling and Estimation of Covariance of Replicated Modulated Cyclical Time Series","publication_year":2013,"publication_date":"2013-01-01","ids":{"openalex":"https://openalex.org/W2089340426","doi":"https://doi.org/10.1109/tsp.2012.2237168","mag":"2089340426"},"language":"en","primary_location":{"id":"doi:10.1109/tsp.2012.2237168","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2012.2237168","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"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 Transactions on Signal Processing","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/A5083945023","display_name":"Sofia C. Olhede","orcid":"https://orcid.org/0000-0003-0061-227X"},"institutions":[{"id":"https://openalex.org/I45129253","display_name":"University College London","ror":"https://ror.org/02jx3x895","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I45129253"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Sofia C. Olhede","raw_affiliation_strings":["Department of Statistical Science, University College London, London, UK","Dept. of Stat. Sci., Univ. Coll. London, London, , UK"],"affiliations":[{"raw_affiliation_string":"Department of Statistical Science, University College London, London, UK","institution_ids":["https://openalex.org/I45129253"]},{"raw_affiliation_string":"Dept. of Stat. Sci., Univ. Coll. London, London, , UK","institution_ids":["https://openalex.org/I45129253"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022587472","display_name":"Hernando Ombao","orcid":"https://orcid.org/0000-0001-7020-8091"},"institutions":[{"id":"https://openalex.org/I204250578","display_name":"University of California, Irvine","ror":"https://ror.org/04gyf1771","country_code":"US","type":"education","lineage":["https://openalex.org/I204250578"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hernando Ombao","raw_affiliation_strings":["Department of Statistics, University of California, Irvine, Irvine, CA, USA","Dept. of Stat., Univ. of California at Irvine, Irvine, CA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Statistics, University of California, Irvine, Irvine, CA, USA","institution_ids":["https://openalex.org/I204250578"]},{"raw_affiliation_string":"Dept. of Stat., Univ. of California at Irvine, Irvine, CA, USA","institution_ids":["https://openalex.org/I204250578"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5083945023"],"corresponding_institution_ids":["https://openalex.org/I45129253"],"apc_list":null,"apc_paid":null,"fwci":1.2862,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":{"value":0.81118478,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"61","issue":"8","first_page":"1944","last_page":"1957"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9968000054359436,"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":0.9968000054359436,"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/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9952999949455261,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9947999715805054,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/cyclostationary-process","display_name":"Cyclostationary process","score":0.9787756204605103},{"id":"https://openalex.org/keywords/multitaper","display_name":"Multitaper","score":0.8692734241485596},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.6436202526092529},{"id":"https://openalex.org/keywords/coherence","display_name":"Coherence (philosophical gambling strategy)","score":0.5889089703559875},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5822423696517944},{"id":"https://openalex.org/keywords/frequency-domain","display_name":"Frequency domain","score":0.4856792688369751},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.43993109464645386},{"id":"https://openalex.org/keywords/akaike-information-criterion","display_name":"Akaike information criterion","score":0.422762930393219},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.4147944450378418},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.4128614068031311},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.38390249013900757},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36945170164108276},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.32752126455307007},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.32707756757736206},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.21989893913269043},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.09465178847312927}],"concepts":[{"id":"https://openalex.org/C178351263","wikidata":"https://www.wikidata.org/wiki/Q3922399","display_name":"Cyclostationary process","level":3,"score":0.9787756204605103},{"id":"https://openalex.org/C2777067715","wikidata":"https://www.wikidata.org/wiki/Q3327726","display_name":"Multitaper","level":2,"score":0.8692734241485596},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.6436202526092529},{"id":"https://openalex.org/C2781181686","wikidata":"https://www.wikidata.org/wiki/Q4226068","display_name":"Coherence (philosophical gambling strategy)","level":2,"score":0.5889089703559875},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5822423696517944},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.4856792688369751},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.43993109464645386},{"id":"https://openalex.org/C126674687","wikidata":"https://www.wikidata.org/wiki/Q1662573","display_name":"Akaike information criterion","level":2,"score":0.422762930393219},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.4147944450378418},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.4128614068031311},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.38390249013900757},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36945170164108276},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.32752126455307007},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32707756757736206},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.21989893913269043},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.09465178847312927},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","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/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tsp.2012.2237168","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2012.2237168","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"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 Transactions on Signal Processing","raw_type":"journal-article"},{"id":"pmh:oai:eprints.ucl.ac.uk.OAI2:1394169","is_oa":false,"landing_page_url":"http://discovery.ucl.ac.uk/1394169/","pdf_url":null,"source":{"id":"https://openalex.org/S4306400024","display_name":"UCL Discovery (University College London)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I45129253","host_organization_name":"University College London","host_organization_lineage":["https://openalex.org/I45129253"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"   IEEE TRANSACTIONS ON SIGNAL PROCESSING , 61  (8)   pp. 1944-1957.   (2013)      ","raw_type":"Article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1179212010","display_name":null,"funder_award_id":"EP/I005250/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"}],"funders":[{"id":"https://openalex.org/F4320334627","display_name":"Engineering and Physical Sciences Research Council","ror":"https://ror.org/0439y7842"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W193385438","https://openalex.org/W568232992","https://openalex.org/W1510659740","https://openalex.org/W1554412071","https://openalex.org/W1585414268","https://openalex.org/W1594924988","https://openalex.org/W1814775863","https://openalex.org/W1971830355","https://openalex.org/W1998244228","https://openalex.org/W2001147521","https://openalex.org/W2006366850","https://openalex.org/W2013657592","https://openalex.org/W2020061675","https://openalex.org/W2044650918","https://openalex.org/W2056306993","https://openalex.org/W2057305056","https://openalex.org/W2057522808","https://openalex.org/W2064603653","https://openalex.org/W2067523983","https://openalex.org/W2074878962","https://openalex.org/W2092123263","https://openalex.org/W2101238942","https://openalex.org/W2116308679","https://openalex.org/W2118033677","https://openalex.org/W2120006607","https://openalex.org/W2122751169","https://openalex.org/W2150763951","https://openalex.org/W2155722796","https://openalex.org/W2160573809","https://openalex.org/W2163899311","https://openalex.org/W2263224228","https://openalex.org/W2481317893","https://openalex.org/W2503267194","https://openalex.org/W3043585151","https://openalex.org/W4211251910","https://openalex.org/W4236173595","https://openalex.org/W4241026100","https://openalex.org/W4250580485","https://openalex.org/W4256217162"],"related_works":["https://openalex.org/W2150800209","https://openalex.org/W2046734432","https://openalex.org/W2114680408","https://openalex.org/W2158795626","https://openalex.org/W3097429946","https://openalex.org/W1578405420","https://openalex.org/W2089340426","https://openalex.org/W1781660451","https://openalex.org/W4301744872","https://openalex.org/W2540764305"],"abstract_inverted_index":{"This":[0],"paper":[1],"introduces":[2],"the":[3,46,67,71,83,89,92,115,135,145,148,170,182,186,193,198,208],"novel":[4],"class":[5,11],"of":[6,12,22,73,91,120,172,192],"modulated":[7,94,199],"cyclostationary":[8,74,95,200],"processes,":[9],"a":[10,20,63,165,177],"nonstationary":[13],"processes":[14,29,75],"exhibiting":[15],"frequency":[16,32,80],"coupling,":[17],"and":[18,76,88,144,151],"proposes":[19],"method":[21,175],"their":[23,79],"estimation":[24,116,174],"from":[25],"repeated":[26],"trials.":[27],"Cyclostationary":[28],"also":[30,205],"exhibit":[31],"correlation":[33],"but":[34,160],"have":[35],"Lo\u00e8ve":[36,149],"spectra":[37,87,150],"whose":[38],"support":[39,81],"lies":[40],"only":[41,156],"on":[42,176],"parallel":[43],"lines":[44],"in":[45,66,82,185],"dual-frequency":[47,84],"plane.":[48,85],"Such":[49],"extremely":[50],"sparse":[51],"structure":[52],"does":[53],"not":[54,161],"adequately":[55],"represent":[56],"many":[57],"biological":[58],"processes.":[59],"Thus,":[60],"we":[61,130,168],"propose":[62],"model":[64,178,201,210],"that,":[65],"time":[68],"domain,":[69],"modulates":[70],"covariance":[72],"consequently":[77],"broadens":[78],"The":[86,190],"cross-coherence":[90,152],"proposed":[93,209],"process":[96],"are":[97,123],"first":[98],"estimated":[99],"using":[100],"multitaper":[101],"methods.":[102],"A":[103],"shrinkage":[104],"procedure":[105],"is":[106],"then":[107],"applied":[108],"to":[109,113,202,218],"each":[110,121],"trial-specific":[111],"estimate":[112],"reduce":[114],"risk.":[117],"Multiple":[118],"trials":[119],"series":[122],"observed.":[124],"When":[125],"combining":[126],"information":[127],"across":[128,153],"trials,":[129],"carefully":[131],"take":[132],"into":[133],"account":[134],"bias":[136],"that":[137,147,179,207,216],"may":[138,155],"be":[139,157,219],"introduced":[140],"by":[141,222],"phase":[142],"misalignment":[143],"fact":[146],"replicates":[154],"\u201csimilar\u201d":[158],"-":[159],"necessarily":[162],"identical.":[163],"In":[164],"simulation":[166],"study,":[167],"illustrate":[169],"performance":[171],"our":[173],"realistically":[180],"captures":[181,211],"features":[183],"observed":[184],"electroencephalogram":[187],"(EEG)":[188],"data.":[189],"application":[191],"inference":[194],"methods":[195],"developed":[196],"for":[197],"EEG":[203],"data":[204],"demonstrates":[206],"statistically":[212],"significant":[213],"cross-frequency":[214],"interactions,":[215],"ought":[217],"further":[220],"examined":[221],"neuroscientists.":[223]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":4},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":1}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
