{"id":"https://openalex.org/W4408380208","doi":"https://doi.org/10.1080/10618600.2025.2473936","title":"A Cepstral Model for Efficient Spectral Analysis of Covariate-Dependent Time Series","display_name":"A Cepstral Model for Efficient Spectral Analysis of Covariate-Dependent Time Series","publication_year":2025,"publication_date":"2025-03-12","ids":{"openalex":"https://openalex.org/W4408380208","doi":"https://doi.org/10.1080/10618600.2025.2473936"},"language":"en","primary_location":{"id":"doi:10.1080/10618600.2025.2473936","is_oa":true,"landing_page_url":"https://doi.org/10.1080/10618600.2025.2473936","pdf_url":"https://www.tandfonline.com/doi/pdf/10.1080/10618600.2025.2473936?needAccess=true","source":{"id":"https://openalex.org/S76159266","display_name":"Journal of Computational and Graphical Statistics","issn_l":"1061-8600","issn":["1061-8600","1537-2715"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computational and Graphical Statistics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://www.tandfonline.com/doi/pdf/10.1080/10618600.2025.2473936?needAccess=true","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5067160952","display_name":"Zeda Li","orcid":"https://orcid.org/0000-0002-0305-6240"},"institutions":[{"id":"https://openalex.org/I141810926","display_name":"Baruch College","ror":"https://ror.org/023qavy03","country_code":"US","type":"education","lineage":["https://openalex.org/I141810926"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Zeda Li","raw_affiliation_strings":["Paul H. Chook Department of Information System and Statistics, Baruch College, The City University of New York"],"raw_orcid":"https://orcid.org/0000-0002-0305-6240","affiliations":[{"raw_affiliation_string":"Paul H. Chook Department of Information System and Statistics, Baruch College, The City University of New York","institution_ids":["https://openalex.org/I141810926"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5114377614","display_name":"Yuexiao Dong","orcid":"https://orcid.org/0000-0003-2269-7745"},"institutions":[{"id":"https://openalex.org/I84392919","display_name":"Temple University","ror":"https://ror.org/00kx1jb78","country_code":"US","type":"education","lineage":["https://openalex.org/I84392919"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yuexiao Dong","raw_affiliation_strings":["Department of Statistics, Operations, and Data Science, Temple University"],"raw_orcid":"https://orcid.org/0000-0003-2269-7745","affiliations":[{"raw_affiliation_string":"Department of Statistics, Operations, and Data Science, Temple University","institution_ids":["https://openalex.org/I84392919"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5067160952"],"corresponding_institution_ids":["https://openalex.org/I141810926"],"apc_list":null,"apc_paid":null,"fwci":1.0383,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.72165833,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"34","issue":"4","first_page":"1612","last_page":"1624"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9991999864578247,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9991999864578247,"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/T13487","display_name":"Statistical and numerical algorithms","score":0.9897000193595886,"subfield":{"id":"https://openalex.org/subfields/2604","display_name":"Applied Mathematics"},"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/T11270","display_name":"Complex Systems and Time Series Analysis","score":0.9793000221252441,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/covariate","display_name":"Covariate","score":0.8393663167953491},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6473454833030701},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5307061076164246},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5090548396110535},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.4422692656517029},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.38558363914489746},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3647176921367645},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3465443253517151},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.24879398941993713},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.08197131752967834}],"concepts":[{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.8393663167953491},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6473454833030701},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5307061076164246},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5090548396110535},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.4422692656517029},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.38558363914489746},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3647176921367645},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3465443253517151},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.24879398941993713},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.08197131752967834},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/10618600.2025.2473936","is_oa":true,"landing_page_url":"https://doi.org/10.1080/10618600.2025.2473936","pdf_url":"https://www.tandfonline.com/doi/pdf/10.1080/10618600.2025.2473936?needAccess=true","source":{"id":"https://openalex.org/S76159266","display_name":"Journal of Computational and Graphical Statistics","issn_l":"1061-8600","issn":["1061-8600","1537-2715"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computational and Graphical Statistics","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1080/10618600.2025.2473936","is_oa":true,"landing_page_url":"https://doi.org/10.1080/10618600.2025.2473936","pdf_url":"https://www.tandfonline.com/doi/pdf/10.1080/10618600.2025.2473936?needAccess=true","source":{"id":"https://openalex.org/S76159266","display_name":"Journal of Computational and Graphical Statistics","issn_l":"1061-8600","issn":["1061-8600","1537-2715"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computational and Graphical Statistics","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G436136984","display_name":null,"funder_award_id":"DMS-2418850","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"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":64,"referenced_works":["https://openalex.org/W146116082","https://openalex.org/W406837113","https://openalex.org/W1540148681","https://openalex.org/W1544274373","https://openalex.org/W1997927852","https://openalex.org/W2001572343","https://openalex.org/W2007098529","https://openalex.org/W2023887324","https://openalex.org/W2025078777","https://openalex.org/W2025100215","https://openalex.org/W2037352103","https://openalex.org/W2044702598","https://openalex.org/W2045077176","https://openalex.org/W2049124732","https://openalex.org/W2049984030","https://openalex.org/W2054121219","https://openalex.org/W2055708756","https://openalex.org/W2057305056","https://openalex.org/W2076379077","https://openalex.org/W2081531193","https://openalex.org/W2086953401","https://openalex.org/W2087377754","https://openalex.org/W2089309985","https://openalex.org/W2098330912","https://openalex.org/W2100848540","https://openalex.org/W2104085366","https://openalex.org/W2110510087","https://openalex.org/W2114377386","https://openalex.org/W2122722706","https://openalex.org/W2131230871","https://openalex.org/W2136554543","https://openalex.org/W2138085925","https://openalex.org/W2141366416","https://openalex.org/W2149267605","https://openalex.org/W2152299486","https://openalex.org/W2152674124","https://openalex.org/W2164772540","https://openalex.org/W2169577279","https://openalex.org/W2188880327","https://openalex.org/W2257698839","https://openalex.org/W2341911512","https://openalex.org/W2509064655","https://openalex.org/W2612089514","https://openalex.org/W2782384783","https://openalex.org/W2785189499","https://openalex.org/W2900832355","https://openalex.org/W2903126092","https://openalex.org/W2962782950","https://openalex.org/W3098291460","https://openalex.org/W3105232361","https://openalex.org/W3119267857","https://openalex.org/W3121261779","https://openalex.org/W3202496196","https://openalex.org/W3213822836","https://openalex.org/W3214883800","https://openalex.org/W4214948526","https://openalex.org/W4256217162","https://openalex.org/W4288049951","https://openalex.org/W4296781176","https://openalex.org/W4297333612","https://openalex.org/W4298872162","https://openalex.org/W4313212506","https://openalex.org/W4321503253","https://openalex.org/W4391418165"],"related_works":["https://openalex.org/W2985746494","https://openalex.org/W4206042385","https://openalex.org/W2511384863","https://openalex.org/W2096089271","https://openalex.org/W2923628599","https://openalex.org/W2109980432","https://openalex.org/W2119012848","https://openalex.org/W2622688551","https://openalex.org/W1550175370","https://openalex.org/W1990205660"],"abstract_inverted_index":{"This":[0],"article":[1,191],"introduces":[2],"a":[3,29,54,69,86,106],"novel":[4],"and":[5,15,28,42,84,130,183],"computationally":[6],"fast":[7],"model":[8,32,58,76,140],"to":[9,35,93,112],"study":[10],"the":[11,37,40,50,60,64,75,103,114,120,125,132,144,154,160,167,173],"association":[12,38],"between":[13,39],"covariates":[14],"power":[16],"spectra":[17],"of":[18,72,81,89,124,135,166],"replicated":[19],"time":[20,82,91],"series.":[21],"A":[22,96],"random":[23],"covariate-dependent":[24],"Cram\u00e9r":[25],"spectral":[26],"representation":[27],"semiparametric":[30],"log-spectral":[31],"are":[33,137,192],"used":[34,111],"quantify":[36],"log-spectra":[41],"covariates.":[43,168],"Each":[44],"replicate-specific":[45,116],"log-spectrum":[46],"is":[47,100,110,141],"represented":[48],"by":[49],"cepstral":[51,61,73,117],"coefficients,":[52,74],"inducing":[53],"cepstral-based":[55,126],"multivariate":[56,127,155],"linear":[57,128,156],"with":[59],"coefficients":[62],"as":[63],"responses.":[65],"By":[66],"using":[67],"only":[68],"small":[70],"number":[71],"parsimoniously":[77],"captures":[78],"frequency":[79],"patterns":[80],"series":[83],"saves":[85],"significant":[87],"amount":[88],"computational":[90,185],"compared":[92],"existing":[94,178],"methods.":[95],"two-stage":[97],"estimation":[98,151],"procedure":[99],"proposed.":[101],"In":[102,119],"first":[104],"stage,":[105,122],"Whittle":[107],"likelihood-based":[108],"approach":[109],"estimate":[113],"truncated":[115],"coefficients.":[118],"second":[121],"parameters":[123],"model,":[129,157],"consequently":[131],"effect":[133],"functions":[134],"covariates,":[136],"estimated.":[138],"The":[139],"flexible":[142],"in":[143],"sense":[145],"that":[146,172],"it":[147],"can":[148],"accommodate":[149],"various":[150],"methods":[152,179],"for":[153,189],"depending":[158],"on":[159],"application,":[161],"domain":[162],"knowledge,":[163],"or":[164],"characteristics":[165],"Numerical":[169],"studies":[170],"confirm":[171],"proposed":[174],"method":[175],"outperforms":[176],"some":[177],"despite":[180],"its":[181],"simplicity":[182],"shorter":[184],"time.":[186],"Supplementary":[187],"materials":[188],"this":[190],"available":[193],"online.":[194]},"counts_by_year":[],"updated_date":"2026-06-15T08:34:33.830935","created_date":"2025-10-10T00:00:00"}
