{"id":"https://openalex.org/W4390045832","doi":"https://doi.org/10.1109/idaacs58523.2023.10348640","title":"Count Predictive Model with Mixed Categorical and Count Explanatory Variables","display_name":"Count Predictive Model with Mixed Categorical and Count Explanatory Variables","publication_year":2023,"publication_date":"2023-09-07","ids":{"openalex":"https://openalex.org/W4390045832","doi":"https://doi.org/10.1109/idaacs58523.2023.10348640"},"language":"en","primary_location":{"id":"doi:10.1109/idaacs58523.2023.10348640","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/idaacs58523.2023.10348640","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 12th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)","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/A5008406294","display_name":"Ev\u017eenie Uglickich","orcid":null},"institutions":[{"id":"https://openalex.org/I4210119419","display_name":"Czech Academy of Sciences, Institute of Information Theory and Automation","ror":"https://ror.org/03h1hsz49","country_code":"CZ","type":"facility","lineage":["https://openalex.org/I202391551","https://openalex.org/I4210119419"]}],"countries":["CZ"],"is_corresponding":true,"raw_author_name":"Ev\u017eenie Uglickich","raw_affiliation_strings":["Institute of Information Theory and Automation of the Czech Academy of Sciences,Department of Signal Processing,Prague,Czech Republic,18208"],"raw_orcid":"https://orcid.org/0000-0003-1764-5924","affiliations":[{"raw_affiliation_string":"Institute of Information Theory and Automation of the Czech Academy of Sciences,Department of Signal Processing,Prague,Czech Republic,18208","institution_ids":["https://openalex.org/I4210119419"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052854984","display_name":"Iv\u00e1n Nagy","orcid":"https://orcid.org/0000-0002-7847-1932"},"institutions":[{"id":"https://openalex.org/I4210119419","display_name":"Czech Academy of Sciences, Institute of Information Theory and Automation","ror":"https://ror.org/03h1hsz49","country_code":"CZ","type":"facility","lineage":["https://openalex.org/I202391551","https://openalex.org/I4210119419"]},{"id":"https://openalex.org/I44504214","display_name":"Czech Technical University in Prague","ror":"https://ror.org/03kqpb082","country_code":"CZ","type":"education","lineage":["https://openalex.org/I44504214"]}],"countries":["CZ"],"is_corresponding":false,"raw_author_name":"Ivan Nagy","raw_affiliation_strings":["Institute of Information Theory and Automation of the Czech Academy of Sciences,Department of Signal Processing,Prague,Czech Republic,18208","Faculty of Transportation Sciences, Czech Technical University, Prague, Czech Republic"],"raw_orcid":"https://orcid.org/0000-0002-7847-1932","affiliations":[{"raw_affiliation_string":"Institute of Information Theory and Automation of the Czech Academy of Sciences,Department of Signal Processing,Prague,Czech Republic,18208","institution_ids":["https://openalex.org/I4210119419"]},{"raw_affiliation_string":"Faculty of Transportation Sciences, Czech Technical University, Prague, Czech Republic","institution_ids":["https://openalex.org/I44504214"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011191614","display_name":"Tetiana Reznychenko","orcid":null},"institutions":[{"id":"https://openalex.org/I44504214","display_name":"Czech Technical University in Prague","ror":"https://ror.org/03kqpb082","country_code":"CZ","type":"education","lineage":["https://openalex.org/I44504214"]}],"countries":["CZ"],"is_corresponding":false,"raw_author_name":"Tetiana Reznychenko","raw_affiliation_strings":["Czech Technical University,Faculty of Transportation Sciences,Prague,Czech Republic,11000"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Czech Technical University,Faculty of Transportation Sciences,Prague,Czech Republic,11000","institution_ids":["https://openalex.org/I44504214"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5008406294"],"corresponding_institution_ids":["https://openalex.org/I4210119419"],"apc_list":null,"apc_paid":null,"fwci":0.3408,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.67786854,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"51","last_page":"56"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9818999767303467,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9818999767303467,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11819","display_name":"Data-Driven Disease Surveillance","score":0.9585000276565552,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9476000070571899,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.8771508932113647},{"id":"https://openalex.org/keywords/count-data","display_name":"Count data","score":0.8107743263244629},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.6022788286209106},{"id":"https://openalex.org/keywords/poisson-regression","display_name":"Poisson regression","score":0.4940109848976135},{"id":"https://openalex.org/keywords/variable","display_name":"Variable (mathematics)","score":0.47442927956581116},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4518654942512512},{"id":"https://openalex.org/keywords/poisson-distribution","display_name":"Poisson distribution","score":0.4444545805454254},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.4407736659049988},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.43293648958206177},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.42258816957473755},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.34658104181289673},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.07380121946334839}],"concepts":[{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.8771508932113647},{"id":"https://openalex.org/C33643355","wikidata":"https://www.wikidata.org/wiki/Q5176731","display_name":"Count data","level":3,"score":0.8107743263244629},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.6022788286209106},{"id":"https://openalex.org/C73269764","wikidata":"https://www.wikidata.org/wiki/Q954529","display_name":"Poisson regression","level":3,"score":0.4940109848976135},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.47442927956581116},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4518654942512512},{"id":"https://openalex.org/C100906024","wikidata":"https://www.wikidata.org/wiki/Q205692","display_name":"Poisson distribution","level":2,"score":0.4444545805454254},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.4407736659049988},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.43293648958206177},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.42258816957473755},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34658104181289673},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.07380121946334839},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/idaacs58523.2023.10348640","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/idaacs58523.2023.10348640","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 12th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W377295446","https://openalex.org/W1257151683","https://openalex.org/W1493790738","https://openalex.org/W1532011672","https://openalex.org/W1581344423","https://openalex.org/W1608397090","https://openalex.org/W1973628995","https://openalex.org/W1976702273","https://openalex.org/W2002011878","https://openalex.org/W2018302799","https://openalex.org/W2032732611","https://openalex.org/W2058390080","https://openalex.org/W2060831261","https://openalex.org/W2119634512","https://openalex.org/W2121024352","https://openalex.org/W2150180906","https://openalex.org/W2158780679","https://openalex.org/W2733680843","https://openalex.org/W2746691464","https://openalex.org/W2753481132","https://openalex.org/W2753666478","https://openalex.org/W2962914532","https://openalex.org/W3036128637","https://openalex.org/W3165665316","https://openalex.org/W4213251304","https://openalex.org/W4285080560","https://openalex.org/W4300750760","https://openalex.org/W4302063968","https://openalex.org/W4302863204","https://openalex.org/W7005508214"],"related_works":["https://openalex.org/W1578743158","https://openalex.org/W3088295586","https://openalex.org/W2060931646","https://openalex.org/W1590941618","https://openalex.org/W3210390693","https://openalex.org/W4280555366","https://openalex.org/W3134844154","https://openalex.org/W2503331259","https://openalex.org/W3096804274","https://openalex.org/W2270206682"],"abstract_inverted_index":{"-":[0],"The":[1,24,86,120],"paper":[2],"considers":[3],"the":[4,43,52,64,71,78,91,95,100,104,109,112],"problem":[5],"of":[6,9,18,34,38,51,56,59,70,77,90,94,103,111,116],"online":[7,75],"prediction":[8,76,129],"a":[10,35,48,57],"count":[11,20,80],"variable":[12,46,107],"based":[13,28],"on":[14,29,63],"real-time":[15],"explanatory":[16,40,45,84,106],"data":[17,126],"mixed":[19],"and":[21,67,124],"categorical":[22,44,105],"nature.":[23],"presented":[25],"solution":[26],"is":[27,88],"(i)":[30],"recursive":[31],"Bayesian":[32],"estimation":[33],"mixture":[36,58],"model":[37,102],"Poisson-distributed":[39],"counts,":[41],"using":[42,81],"as":[47],"measurable":[49],"pointer":[50],"mixture,":[53],"(ii)":[54],"construction":[55],"local":[60],"Poisson":[61],"regressions":[62],"clustered":[65],"data,":[66],"(iii)":[68],"use":[69],"pre-estimated":[72],"mixtures":[73],"for":[74],"target":[79],"actual":[82],"measured":[83],"data.":[85],"latter":[87],"one":[89],"main":[92],"contributions":[93],"proposed":[96],"approach.":[97],"In":[98],"addition,":[99],"dynamic":[101],"preserves":[108],"functionality":[110],"algorithm":[113],"in":[114],"case":[115],"its":[117],"measurement":[118],"failure.":[119],"experiments":[121],"with":[122],"simulations":[123],"real":[125],"report":[127],"lower":[128],"errors":[130],"compared":[131],"to":[132],"theoretical":[133],"counterparts.":[134]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2025-12-25T23:11:45.687758","created_date":"2025-10-10T00:00:00"}