{"id":"https://openalex.org/W4406461287","doi":"https://doi.org/10.1109/bigdata62323.2024.10825444","title":"Luckiness Normalized Maximum Likelihood-based Change Detection for High-dimensional Graphical Models with Missing Data","display_name":"Luckiness Normalized Maximum Likelihood-based Change Detection for High-dimensional Graphical Models with Missing Data","publication_year":2024,"publication_date":"2024-12-15","ids":{"openalex":"https://openalex.org/W4406461287","doi":"https://doi.org/10.1109/bigdata62323.2024.10825444"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata62323.2024.10825444","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825444","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-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/A5097055569","display_name":"Zhongyi Que","orcid":"https://orcid.org/0000-0002-6156-5115"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Zhongyi Que","raw_affiliation_strings":["The University of Tokyo,Graduate School of Information Science and Technology,Tokyo,Japan"],"affiliations":[{"raw_affiliation_string":"The University of Tokyo,Graduate School of Information Science and Technology,Tokyo,Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074226898","display_name":"Linchuan Xu","orcid":"https://orcid.org/0000-0003-2224-2425"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Linchuan Xu","raw_affiliation_strings":["The University of Tokyo,Graduate School of Information Science and Technology,Tokyo,Japan"],"affiliations":[{"raw_affiliation_string":"The University of Tokyo,Graduate School of Information Science and Technology,Tokyo,Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021981442","display_name":"Kenji Yamanishi","orcid":"https://orcid.org/0000-0001-7370-9991"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kenji Yamanishi","raw_affiliation_strings":["The University of Tokyo,Graduate School of Information Science and Technology,Tokyo,Japan"],"affiliations":[{"raw_affiliation_string":"The University of Tokyo,Graduate School of Information Science and Technology,Tokyo,Japan","institution_ids":["https://openalex.org/I74801974"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5097055569"],"corresponding_institution_ids":["https://openalex.org/I74801974"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.26406511,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"84","last_page":"93"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9889000058174133,"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/T10136","display_name":"Statistical Methods and Inference","score":0.9889000058174133,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9810000061988831,"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/T11236","display_name":"Control Systems and Identification","score":0.9646999835968018,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5943737626075745},{"id":"https://openalex.org/keywords/change-detection","display_name":"Change detection","score":0.5932567715644836},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.5863611698150635},{"id":"https://openalex.org/keywords/graphical-model","display_name":"Graphical model","score":0.5763983130455017},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.5627951622009277},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.46728119254112244},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.33516979217529297},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3222411870956421},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.31777477264404297},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.31673842668533325},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.23253899812698364},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.1865464448928833},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.062394917011260986}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5943737626075745},{"id":"https://openalex.org/C203595873","wikidata":"https://www.wikidata.org/wiki/Q25389927","display_name":"Change detection","level":2,"score":0.5932567715644836},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.5863611698150635},{"id":"https://openalex.org/C155846161","wikidata":"https://www.wikidata.org/wiki/Q1143367","display_name":"Graphical model","level":2,"score":0.5763983130455017},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.5627951622009277},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.46728119254112244},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33516979217529297},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3222411870956421},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.31777477264404297},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.31673842668533325},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.23253899812698364},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.1865464448928833},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.062394917011260986}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata62323.2024.10825444","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825444","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4699999988079071,"display_name":"Climate action","id":"https://metadata.un.org/sdg/13"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":48,"referenced_works":["https://openalex.org/W162028236","https://openalex.org/W1610612296","https://openalex.org/W1965048988","https://openalex.org/W1991484728","https://openalex.org/W1992362262","https://openalex.org/W2054658115","https://openalex.org/W2056345861","https://openalex.org/W2077870633","https://openalex.org/W2091825839","https://openalex.org/W2093242491","https://openalex.org/W2131242330","https://openalex.org/W2132555912","https://openalex.org/W2133396774","https://openalex.org/W2137446376","https://openalex.org/W2142635246","https://openalex.org/W2152589362","https://openalex.org/W2167200240","https://openalex.org/W2167737547","https://openalex.org/W2168175751","https://openalex.org/W2240788666","https://openalex.org/W2583850415","https://openalex.org/W2585285071","https://openalex.org/W2596585349","https://openalex.org/W2622422944","https://openalex.org/W2846522193","https://openalex.org/W2963709432","https://openalex.org/W3012131212","https://openalex.org/W3041235851","https://openalex.org/W3098547022","https://openalex.org/W3098724218","https://openalex.org/W3103368540","https://openalex.org/W3103475671","https://openalex.org/W3104130497","https://openalex.org/W3105104252","https://openalex.org/W3108706354","https://openalex.org/W3123545922","https://openalex.org/W3175593692","https://openalex.org/W3197777881","https://openalex.org/W4301588852","https://openalex.org/W4320858528","https://openalex.org/W4386743050","https://openalex.org/W4388323202","https://openalex.org/W6675276251","https://openalex.org/W6679560219","https://openalex.org/W6679719908","https://openalex.org/W6684098844","https://openalex.org/W6752649438","https://openalex.org/W6849523142"],"related_works":["https://openalex.org/W4380150146","https://openalex.org/W3024870410","https://openalex.org/W2410652950","https://openalex.org/W4283773154","https://openalex.org/W3139174110","https://openalex.org/W4289597203","https://openalex.org/W2085630472","https://openalex.org/W4285201053","https://openalex.org/W2753779043","https://openalex.org/W2110322980"],"abstract_inverted_index":{"This":[0,11],"study":[1],"focuses":[2],"on":[3,96,125],"detecting":[4],"dependency":[5],"changes":[6,20],"in":[7,21,61,70,158,175],"multivariate":[8,62],"time":[9],"series.":[10],"is":[12],"a":[13,112,155],"practically":[14],"important":[15],"issue":[16,66],"because,":[17],"for":[18,44,140],"example,":[19],"the":[22,51,58,74,77,80,86,102,105,126,149,170,182],"relationships":[23],"between":[24],"several":[25,94],"stocks":[26],"may":[27],"lead":[28],"to":[29,56,67,107,146,172],"early":[30],"warning":[31],"of":[32,79,104,160,184,189],"critical":[33],"economic":[34],"events.":[35],"The":[36],"corresponding":[37],"change":[38,68,98,122,151],"detection":[39,69],"methods":[40,191],"should":[41],"be":[42],"feasible":[43],"addressing":[45],"high-dimensional":[46,109],"cases":[47,75,174],"online.":[48],"We":[49,143,168,180],"use":[50],"Gaussian":[52],"graphical":[53,81],"model":[54,82,106],"(GGM)":[55],"represent":[57],"conditional":[59],"dependencies":[60],"and":[63,72,134,153,163,196],"reduce":[64],"our":[65,185],"GGM":[71,97],"consider":[73],"with":[76,187],"size":[78,88],"significantly":[83],"larger":[84],"than":[85],"sample":[87],"as":[89],"high-dimensional.":[90],"Although":[91],"there":[92],"are":[93],"studies":[95],"detection,":[99],"adequately":[100],"controlling":[101],"sparsity":[103],"handle":[108],"data":[110],"remains":[111],"significant":[113],"challenge.":[114],"To":[115],"address":[116],"this":[117],"problem,":[118],"we":[119],"introduce":[120],"new":[121],"statistics":[123,152],"based":[124],"luckiness":[127],"normalized":[128],"maximum":[129],"likelihood":[130],"(LNML)":[131],"code":[132],"length":[133,137],"minimum":[135],"description":[136],"(MDL)":[138],"adapted":[139],"sparse":[141],"modeling.":[142],"demonstrate":[144],"how":[145],"efficiently":[147],"calculate":[148],"LNML-based":[150],"provide":[154],"theoretical":[156],"guarantee":[157],"terms":[159],"Type":[161,164],"I":[162],"II":[165],"error":[166],"probabilities.":[167],"expand":[169],"methodology":[171,186],"include":[173],"which":[176],"missing":[177],"values":[178],"exist.":[179],"compare":[181],"effectiveness":[183],"that":[188],"conventional":[190],"through":[192],"experiments":[193],"using":[194],"synthetic":[195],"real-world":[197],"datasets.":[198]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
