{"id":"https://openalex.org/W3180103161","doi":null,"title":"On the connection between the distribution of eigenvalues in multiple correspondence analysis and log-linear analysis","display_name":"On the connection between the distribution of eigenvalues in multiple correspondence analysis and log-linear analysis","publication_year":2003,"publication_date":"2003-06-29","ids":{"openalex":"https://openalex.org/W3180103161","mag":"3180103161"},"language":"en","primary_location":{"id":"pmh:oai:HAL:hal-01124808v1","doi":null,"is_oa":false,"landing_page_url":"https://hal.science/hal-01124808","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I4405252976","host_organization_name":"Centre pour la Communication Scientifique Directe","host_organization_lineage":["https://openalex.org/I4405252976"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"CARME2003: Correspondence Analysis and Related Methods, Jun 2003, Barcelone, Spain","raw_type":"info:eu-repo/semantics/conferenceObject"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":[],"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/A5061948103","display_name":"Salwa Benammou","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I8636806","display_name":"University of Sousse","ror":"https://ror.org/00dmpgj58","country_code":"TN","type":"education","lineage":["https://openalex.org/I8636806"]}],"countries":["TN"],"is_corresponding":false,"raw_author_name":"Benammou, Salwa","raw_affiliation_strings":["Universite de Sousse"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universite de Sousse","institution_ids":["https://openalex.org/I8636806"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019375557","display_name":"Gilbert Saporta","orcid":"https://orcid.org/0000-0002-3406-5887","observed_orcids":["https://orcid.org/0000-0002-3406-5887"]},"institutions":[{"id":"https://openalex.org/I4210145724","display_name":"Centre d'\u00e9tudes et de recherche en informatique et communications","ror":"https://ror.org/044j5mm75","country_code":"FR","type":"facility","lineage":["https://openalex.org/I124158823","https://openalex.org/I4210134562","https://openalex.org/I4210145724"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Saporta, Gilbert","raw_affiliation_strings":["CEDRIC. M\u00e9thodes statistiques de data-mining et apprentissage"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CEDRIC. M\u00e9thodes statistiques de data-mining et apprentissage","institution_ids":["https://openalex.org/I4210145724"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.40780142,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13141","display_name":"Statistical Methods and Applications","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T13141","display_name":"Statistical Methods and Applications","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"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/T11871","display_name":"Advanced Statistical Methods and Models","score":0.00019999999494757503,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"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/T12303","display_name":"Tensor decomposition and applications","score":0.0,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational 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/multiple-correspondence-analysis","display_name":"multiple correspondence analysis","score":0.9929999709129333},{"id":"https://openalex.org/keywords/log-linear-models","display_name":"log-linear models","score":0.9919999837875366},{"id":"https://openalex.org/keywords/eigenvalue-distribution","display_name":"eigenvalue distribution","score":0.9879999756813049},{"id":"https://openalex.org/keywords/multiway-contingency-tables","display_name":"multidimensional contingency tables","score":0.8679999709129333},{"id":"https://openalex.org/keywords/asymptotic-normality","display_name":"asymptotic normality","score":0.8569999933242798},{"id":"https://openalex.org/keywords/independence-hypothesis","display_name":"independence hypothesis","score":0.7059999704360962},{"id":"https://openalex.org/keywords/interaction-effects","display_name":"interaction effects","score":0.7009999752044678},{"id":"https://openalex.org/keywords/system-identification","display_name":"system identification","score":0.5199999809265137}],"concepts":[{"id":"https://openalex.org/C13355873","wikidata":"https://www.wikidata.org/wiki/Q2920850","display_name":"Connection (principal bundle)","level":2,"score":0.7314568161964417},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.6207616925239563},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.48290276527404785},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.4526906907558441},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.39735889434814453},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3332730531692505},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.21345919370651245},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.1093960702419281},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.10647344589233398},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:HAL:hal-01124808v1","doi":null,"is_oa":false,"landing_page_url":"https://hal.science/hal-01124808","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I4405252976","host_organization_name":"Centre pour la Communication Scientifique Directe","host_organization_lineage":["https://openalex.org/I4405252976"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"CARME2003: Correspondence Analysis and Related Methods, Jun 2003, Barcelone, Spain","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7097762013","https://openalex.org/W2333571660","https://openalex.org/W1981783519","https://openalex.org/W2951001589","https://openalex.org/W2047220320","https://openalex.org/W2790023415","https://openalex.org/W2057968399","https://openalex.org/W2969099166","https://openalex.org/W835367354","https://openalex.org/W4309498632"],"abstract_inverted_index":{"Multiple":[0,34],"Correspondence":[1,35],"Analysis":[2,36],"(MCA)":[3],"and":[4,18,137],"Log-Linear":[5,22,67,90],"modelling":[6],"are":[7,24,72],"two":[8,60,70],"techniques":[9],"of":[10,20,32,81,100],"multi-way":[11],"contingency":[12],"table":[13],"analysis":[14],"having":[15],"different":[16],"problematics":[17],"fields":[19],"applications.":[21],"models":[23],"profitably":[25],"when":[26,85],"applied":[27],"to":[28,54],"a":[29,88,121],"small":[30],"number":[31],"variables.":[33],"is":[37,44,51,113,130],"useful":[38],"in":[39,76,83,105,146],"large":[40],"tables.":[41],"This":[42,125],"efficiency":[43],"balanced":[45],"by":[46,66,97,144],"the":[47,79,141],"fact":[48],"that":[49],"MCA":[50,84],"not":[52],"able":[53],"explicit":[55],"relations":[56],"between":[57,135],"more":[58,133],"than":[59],"variables,":[61,136],"as":[62],"can":[63,139],"be":[64],"done":[65],"modelling.":[68],"The":[69,117],"approaches":[71],"complementary.":[73],"We":[74,102],"present":[75],"this":[77,95],"paper":[78],"distribution":[80],"eigenvalues":[82,118],"data":[86,145],"fit":[87],"known":[89],"model,":[91],"then":[92],"we":[93,138],"induct":[94],"model":[96,142],"successive":[98],"utilisation":[99],"MCA.":[101],"demonstrates":[103],"that,":[104],"MCA,":[106],"under":[107],"independence":[108],"hypothesis":[109],"each":[110],"observed":[111],"eigenvalue":[112],"asymptotically":[114],"normally":[115],"distributed.":[116],"diagram":[119],"takes":[120],"very":[122],"particular":[123,148],"shape.":[124],"shape":[126],"changes":[127],"if":[128],"there":[129],"one":[131],"or":[132],"interaction":[134],"recognize":[140],"fitted":[143],"some":[147],"cases.":[149]},"counts_by_year":[],"updated_date":"2026-11-08T12:12:28.789561","created_date":"2021-07-19T00:00:00"}
