{"id":"https://openalex.org/W2169348602","doi":"https://doi.org/10.1109/isbi.2004.1398752","title":"Penalized partially linear models using orthonormal wavelet bases with an application to fMRI time series","display_name":"Penalized partially linear models using orthonormal wavelet bases with an application to fMRI time series","publication_year":2005,"publication_date":"2005-04-06","ids":{"openalex":"https://openalex.org/W2169348602","doi":"https://doi.org/10.1109/isbi.2004.1398752","mag":"2169348602"},"language":"en","primary_location":{"id":"doi:10.1109/isbi.2004.1398752","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi.2004.1398752","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano (IEEE Cat No. 04EX821)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://hal.science/hal-00263558","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5109215397","display_name":"M.J. Fadili","orcid":null},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I4210139483","display_name":"GREYC","ror":"https://ror.org/043749971","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I1294671590","https://openalex.org/I194210350","https://openalex.org/I4210105918","https://openalex.org/I4210105918","https://openalex.org/I4210139483","https://openalex.org/I4210159245","https://openalex.org/I98702875"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"M.J. Fadili","raw_affiliation_strings":["GREYC UMR CNRS 6072, Caen, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"GREYC UMR CNRS 6072, Caen, France","institution_ids":["https://openalex.org/I1294671590","https://openalex.org/I4210139483"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041538919","display_name":"Edward T. Bullmore","orcid":"https://orcid.org/0000-0002-8955-8283"},"institutions":[{"id":"https://openalex.org/I241749","display_name":"University of Cambridge","ror":"https://ror.org/013meh722","country_code":"GB","type":"education","lineage":["https://openalex.org/I241749"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"E.T. Bullmore","raw_affiliation_strings":["Brain Mapping Unit, University of Cambridge, Cambridge, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Brain Mapping Unit, University of Cambridge, Cambridge, UK","institution_ids":["https://openalex.org/I241749"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.29050431,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"2","issue":null,"first_page":"1171","last_page":"1174"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.998199999332428,"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"}},"topics":[{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.998199999332428,"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"}},{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9962999820709229,"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":0.9883000254631042,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.7985929250717163},{"id":"https://openalex.org/keywords/nonparametric-statistics","display_name":"Nonparametric statistics","score":0.6807905435562134},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.642630934715271},{"id":"https://openalex.org/keywords/orthonormal-basis","display_name":"Orthonormal basis","score":0.5904487371444702},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4972701370716095},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4517882466316223},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.4510502219200134},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.44969895482063293},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.39945608377456665},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.33758753538131714},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3307666778564453},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1482396125793457}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.7985929250717163},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.6807905435562134},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.642630934715271},{"id":"https://openalex.org/C5806529","wikidata":"https://www.wikidata.org/wiki/Q2365325","display_name":"Orthonormal basis","level":2,"score":0.5904487371444702},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4972701370716095},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4517882466316223},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.4510502219200134},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.44969895482063293},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.39945608377456665},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.33758753538131714},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3307666778564453},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1482396125793457},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/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/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/isbi.2004.1398752","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi.2004.1398752","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano (IEEE Cat No. 04EX821)","raw_type":"proceedings-article"},{"id":"pmh:oai:HAL:hal-00263558v1","is_oa":true,"landing_page_url":"https://hal.science/hal-00263558","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE International symposium on biomedical imaging, 2004, Arlington, United States. pp.1171-1174, ⟨10.1109/ISBI.2004.1398752⟩","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":{"id":"pmh:oai:HAL:hal-00263558v1","is_oa":true,"landing_page_url":"https://hal.science/hal-00263558","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE International symposium on biomedical imaging, 2004, Arlington, United States. pp.1171-1174, ⟨10.1109/ISBI.2004.1398752⟩","raw_type":"info:eu-repo/semantics/conferenceObject"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.6499999761581421,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W135795571","https://openalex.org/W1486911799","https://openalex.org/W1824682467","https://openalex.org/W1972163814","https://openalex.org/W2123840685","https://openalex.org/W2166143117"],"related_works":["https://openalex.org/W2370850565","https://openalex.org/W2381894435","https://openalex.org/W2296214397","https://openalex.org/W2951195293","https://openalex.org/W4243114048","https://openalex.org/W1975945902","https://openalex.org/W2111545434","https://openalex.org/W2392110000","https://openalex.org/W4381052368","https://openalex.org/W953696202"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3],"consider":[4],"modeling":[5],"the":[6,24,28,37,52,67,86,91],"nonparametric":[7,25,38],"component":[8],"in":[9,27,65],"partially":[10],"linear":[11],"models":[12],"(PLM)":[13],"using":[14],"orthogonal":[15],"wavelet":[16,29],"expansions.":[17],"We":[18,70],"introduce":[19],"a":[20],"regularized":[21],"estimator":[22,94],"of":[23,90],"part":[26,39],"domain.":[30],"The":[31,93],"key":[32],"innovation":[33],"here":[34],"is":[35,81,95],"that":[36,105],"can":[40],"be":[41],"efficiently":[42],"estimated":[43],"by":[44],"choosing":[45],"an":[46,72],"appropriate":[47],"penalty":[48],"function":[49],"for":[50],"which":[51],"hard":[53],"and":[54,112],"soft":[55],"thresholding":[56],"estimators":[57],"are":[58,106],"particular":[59],"cases.":[60],"This":[61],"avoids":[62],"excessive":[63],"bias":[64],"estimating":[66],"parametric":[68],"component.":[69],"give":[71],"efficient":[73],"estimation":[74],"algorithm.":[75],"A":[76],"large":[77],"scale":[78],"simulation":[79],"study":[80],"also":[82],"conducted":[83],"to":[84,98,108],"illustrate":[85],"finite":[87],"sample":[88],"properties":[89],"estimator.":[92],"finally":[96],"applied":[97],"real":[99],"neurophysiological":[100],"functional":[101],"MRI":[102],"data":[103],"sets":[104],"suspected":[107],"contain":[109],"both":[110],"smooth":[111],"transient":[113],"drift":[114],"features.":[115]},"counts_by_year":[{"year":2019,"cited_by_count":1}],"updated_date":"2026-08-26T07:47:46.906454","created_date":"2025-10-10T00:00:00"}