{"id":"https://openalex.org/W2121175244","doi":"https://doi.org/10.1007/s00180-010-0190-8","title":"Binary geometric process model for the modeling of longitudinal binary data with trend","display_name":"Binary geometric process model for the modeling of longitudinal binary data with trend","publication_year":2010,"publication_date":"2010-04-29","ids":{"openalex":"https://openalex.org/W2121175244","doi":"https://doi.org/10.1007/s00180-010-0190-8","mag":"2121175244"},"language":"en","primary_location":{"id":"doi:10.1007/s00180-010-0190-8","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00180-010-0190-8","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00180-010-0190-8.pdf","source":{"id":"https://openalex.org/S8500805","display_name":"Computational Statistics","issn_l":"0943-4062","issn":["0943-4062","1613-9658"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computational Statistics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s00180-010-0190-8.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5055096066","display_name":"Jennifer Chan","orcid":"https://orcid.org/0000-0002-3167-6586"},"institutions":[{"id":"https://openalex.org/I129604602","display_name":"University of Sydney","ror":"https://ror.org/0384j8v12","country_code":"AU","type":"education","lineage":["https://openalex.org/I129604602"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Jennifer S. K. Chan","raw_affiliation_strings":["The University of Sydney, Sydney, NSW, Australia","The University of Sydney"],"affiliations":[{"raw_affiliation_string":"The University of Sydney, Sydney, NSW, Australia","institution_ids":["https://openalex.org/I129604602"]},{"raw_affiliation_string":"The University of Sydney","institution_ids":["https://openalex.org/I129604602"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038979297","display_name":"Doris Y. P. Leung","orcid":"https://orcid.org/0000-0003-0138-8839"},"institutions":[{"id":"https://openalex.org/I889458895","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86","country_code":"HK","type":"education","lineage":["https://openalex.org/I889458895"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Doris Y. P. Leung","raw_affiliation_strings":["The University of Hong Kong, Hong Kong, China","The University of Hong Kong"],"affiliations":[{"raw_affiliation_string":"The University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I889458895"]},{"raw_affiliation_string":"The University of Hong Kong","institution_ids":["https://openalex.org/I889458895"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5055096066"],"corresponding_institution_ids":["https://openalex.org/I129604602"],"apc_list":{"value":2490,"currency":"EUR","value_usd":3090},"apc_paid":{"value":2490,"currency":"EUR","value_usd":3090},"fwci":1.4086,"has_fulltext":true,"cited_by_count":10,"citation_normalized_percentile":{"value":0.85565332,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"25","issue":"3","first_page":"505","last_page":"536"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9900000095367432,"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":0.9900000095367432,"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/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.9891999959945679,"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/T10136","display_name":"Statistical Methods and Inference","score":0.9884999990463257,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/binary-data","display_name":"Binary data","score":0.6720936298370361},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.6062114238739014},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.588564932346344},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5102035999298096},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5068715214729309},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.4785533547401428},{"id":"https://openalex.org/keywords/binary-independence-model","display_name":"Binary Independence Model","score":0.4708295166492462},{"id":"https://openalex.org/keywords/statistical-model","display_name":"Statistical model","score":0.44583606719970703},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.44572579860687256},{"id":"https://openalex.org/keywords/statistical-inference","display_name":"Statistical inference","score":0.44017642736434937},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.4242875874042511},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4108278453350067},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.35867220163345337},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3363800346851349},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.24523961544036865}],"concepts":[{"id":"https://openalex.org/C2779190172","wikidata":"https://www.wikidata.org/wiki/Q4913888","display_name":"Binary data","level":3,"score":0.6720936298370361},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.6062114238739014},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.588564932346344},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5102035999298096},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5068715214729309},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.4785533547401428},{"id":"https://openalex.org/C37061001","wikidata":"https://www.wikidata.org/wiki/Q3531721","display_name":"Binary Independence Model","level":3,"score":0.4708295166492462},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.44583606719970703},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.44572579860687256},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.44017642736434937},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4242875874042511},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4108278453350067},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.35867220163345337},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3363800346851349},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.24523961544036865},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1007/s00180-010-0190-8","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00180-010-0190-8","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00180-010-0190-8.pdf","source":{"id":"https://openalex.org/S8500805","display_name":"Computational Statistics","issn_l":"0943-4062","issn":["0943-4062","1613-9658"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computational Statistics","raw_type":"journal-article"},{"id":"pmh:oai:RePEc:spr:compst:v:25:y:2010:i:3:p:505-536","is_oa":false,"landing_page_url":"http://hdl.handle.net/10.1007/s00180-010-0190-8","pdf_url":null,"source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},{"id":"pmh:oai:hub.hku.hk:10722/124058","is_oa":true,"landing_page_url":"http://hdl.handle.net/10722/124058","pdf_url":null,"source":{"id":"https://openalex.org/S4377196271","display_name":"The HKU Scholars Hub (University of Hong Kong)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I889458895","host_organization_name":"University of Hong Kong","host_organization_lineage":["https://openalex.org/I889458895"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Article"}],"best_oa_location":{"id":"doi:10.1007/s00180-010-0190-8","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00180-010-0190-8","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00180-010-0190-8.pdf","source":{"id":"https://openalex.org/S8500805","display_name":"Computational Statistics","issn_l":"0943-4062","issn":["0943-4062","1613-9658"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computational Statistics","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2121175244.pdf","grobid_xml":"https://content.openalex.org/works/W2121175244.grobid-xml"},"referenced_works_count":37,"referenced_works":["https://openalex.org/W109881820","https://openalex.org/W625843406","https://openalex.org/W1524004264","https://openalex.org/W1965667126","https://openalex.org/W1967578676","https://openalex.org/W1973018191","https://openalex.org/W1979387516","https://openalex.org/W1984678267","https://openalex.org/W1992779201","https://openalex.org/W2017899835","https://openalex.org/W2027690728","https://openalex.org/W2041609908","https://openalex.org/W2042894453","https://openalex.org/W2045747328","https://openalex.org/W2045926641","https://openalex.org/W2056760934","https://openalex.org/W2067244657","https://openalex.org/W2068831362","https://openalex.org/W2069591887","https://openalex.org/W2076836388","https://openalex.org/W2080598760","https://openalex.org/W2092310959","https://openalex.org/W2130416410","https://openalex.org/W2138309709","https://openalex.org/W2165137363","https://openalex.org/W2170161279","https://openalex.org/W2320179770","https://openalex.org/W2523345256","https://openalex.org/W2556796235","https://openalex.org/W4212910029","https://openalex.org/W4234599031","https://openalex.org/W4238059788","https://openalex.org/W4238595107","https://openalex.org/W4246157667","https://openalex.org/W4251644969","https://openalex.org/W4300126076","https://openalex.org/W4388319614"],"related_works":["https://openalex.org/W2753218748","https://openalex.org/W3183730129","https://openalex.org/W2953280030","https://openalex.org/W2889562828","https://openalex.org/W321860625","https://openalex.org/W2950480453","https://openalex.org/W2887284286","https://openalex.org/W4237934094","https://openalex.org/W4361195775","https://openalex.org/W4297899982"],"abstract_inverted_index":{"We":[0],"propose":[1],"the":[2,24,29,37,41,46,64,70,98,102,105,111],"Binary":[3],"Geometric":[4,15],"Process":[5,16],"(BGP)":[6],"model":[7,18,50,79,107],"for":[8,63],"longitudinal":[9],"binary":[10,54],"data":[11,55,87],"with":[12],"trends.":[13],"The":[14,48,60,78],"(GP)":[17],"contains":[19],"two":[20],"components":[21],"to":[22,53],"capture":[23],"dynamics":[25],"on":[26],"a":[27,57],"trend:":[28],"mean":[30],"of":[31,45],"an":[32],"underlying":[33],"renewal":[34],"process":[35],"and":[36,43,75,85,96],"ratio":[38],"which":[39],"measures":[40],"direction":[42],"strength":[44],"trend.":[47],"GP":[49],"is":[51,67,80,108],"extended":[52],"using":[56,69],"latent":[58],"GP.":[59],"statistical":[61],"inference":[62],"BGP":[65,106],"models":[66],"conducted":[68],"least-square,":[71],"maximum":[72],"likelihood":[73],"(ML)":[74],"Bayesian":[76],"methods.":[77],"demonstrated":[81],"through":[82],"simulation":[83],"studies":[84],"real":[86],"analyzes.":[88],"Results":[89],"reveal":[90],"that":[91,97],"all":[92],"estimators":[93],"perform":[94],"satisfactorily":[95],"ML":[99],"estimator":[100],"performs":[101],"best.":[103],"Moreover":[104],"better":[109],"than":[110],"ordinary":[112],"logistic":[113],"regression":[114],"model.":[115]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":2}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
