{"id":"https://openalex.org/W7215712596","doi":"https://doi.org/10.20956/ejsa.vi.25426","title":"Pemodelan Mixed Geographically Weighted Regression yang Mengandung Multikolinearitas dengan Regresi Ridge","display_name":"Pemodelan Mixed Geographically Weighted Regression yang Mengandung Multikolinearitas dengan Regresi Ridge","publication_year":2023,"publication_date":"2023-02-14","ids":{"openalex":"https://openalex.org/W7215712596","doi":"https://doi.org/10.20956/ejsa.vi.25426"},"language":"en","primary_location":{"id":"pmh:oai:ojs.unhas:article/25426","doi":"https://doi.org/10.20956/ejsa.vi.25426","is_oa":true,"landing_page_url":"https://journal.unhas.ac.id/index.php/index/article/view/25426","pdf_url":null,"source":{"id":"https://openalex.org/S4210199200","display_name":"ESTIMASI Journal of Statistics and Its Application","issn_l":"2721-379X","issn":["2721-379X","2721-3803"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/P4310315277","host_organization_name":"Hasanuddin University","host_organization_lineage":["https://openalex.org/P4310315277"],"host_organization_lineage_names":["Hasanuddin University"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2721-3803","raw_type":"info:eu-repo/semantics/article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://journal.unhas.ac.id/index.php/index/article/view/25426","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5155316069","display_name":"Suritman","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Suritman","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5155382667","display_name":"Raupong","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Raupong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5017795671","display_name":"Anisa Kalondeng","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kalondeng, Anisa","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1738,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.50719213,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":93},"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/T11911","display_name":"Spatial and Panel Data Analysis","score":0.9911999702453613,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11911","display_name":"Spatial and Panel Data Analysis","score":0.9911999702453613,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11871","display_name":"Advanced Statistical Methods and Models","score":0.007400000002235174,"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/T13141","display_name":"Statistical Methods and Applications","score":9.999999747378752e-05,"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/mixed-geographically-weighted-regression","display_name":"mixed geographically weighted regression","score":0.9940000176429749},{"id":"https://openalex.org/keywords/ridge-regression","display_name":"ridge regression","score":0.8970000147819519},{"id":"https://openalex.org/keywords/multicollinearity","display_name":"multicollinearity","score":1.0},{"id":"https://openalex.org/keywords/spatial-heterogeneity","display_name":"spatial heterogeneity","score":0.9129999876022339},{"id":"https://openalex.org/keywords/poverty","display_name":"poverty","score":0.9010000228881836},{"id":"https://openalex.org/keywords/low-income-populations","display_name":"low-income populations","score":0.7099999785423279},{"id":"https://openalex.org/keywords/south-sulawesi","display_name":"South Sulawesi","score":0.9169999957084656}],"concepts":[{"id":"https://openalex.org/C189285262","wikidata":"https://www.wikidata.org/wiki/Q1332350","display_name":"Multicollinearity","level":3,"score":0.9168999791145325},{"id":"https://openalex.org/C32277403","wikidata":"https://www.wikidata.org/wiki/Q740445","display_name":"Ridge","level":2,"score":0.7398999929428101},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.6310999989509583},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.618399977684021},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5975000262260437},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.5845000147819519},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.5336999893188477},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.46070000529289246},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.4345000088214874},{"id":"https://openalex.org/C57381214","wikidata":"https://www.wikidata.org/wiki/Q55631393","display_name":"Regression diagnostic","level":4,"score":0.4002000093460083},{"id":"https://openalex.org/C60316415","wikidata":"https://www.wikidata.org/wiki/Q6664520","display_name":"Local regression","level":4,"score":0.35429999232292175},{"id":"https://openalex.org/C2910321205","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Geographically Weighted Regression","level":2,"score":0.3228999972343445},{"id":"https://openalex.org/C27574286","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Variables","level":2,"score":0.320499986410141},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.31529998779296875},{"id":"https://openalex.org/C2776291640","wikidata":"https://www.wikidata.org/wiki/Q2912517","display_name":"Value (mathematics)","level":2,"score":0.31060001254081726},{"id":"https://openalex.org/C90157343","wikidata":"https://www.wikidata.org/wiki/Q5188196","display_name":"Cross-sectional regression","level":4,"score":0.31040000915527344},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.31029999256134033},{"id":"https://openalex.org/C120068334","wikidata":"https://www.wikidata.org/wiki/Q45343","display_name":"Polynomial regression","level":3,"score":0.25380000472068787}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:ojs.unhas:article/25426","doi":"https://doi.org/10.20956/ejsa.vi.25426","is_oa":true,"landing_page_url":"https://journal.unhas.ac.id/index.php/index/article/view/25426","pdf_url":null,"source":{"id":"https://openalex.org/S4210199200","display_name":"ESTIMASI Journal of Statistics and Its Application","issn_l":"2721-379X","issn":["2721-379X","2721-3803"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/P4310315277","host_organization_name":"Hasanuddin University","host_organization_lineage":["https://openalex.org/P4310315277"],"host_organization_lineage_names":["Hasanuddin University"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2721-3803","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"pmh:oai:ojs.unhas:article/25426","doi":"https://doi.org/10.20956/ejsa.vi.25426","is_oa":true,"landing_page_url":"https://journal.unhas.ac.id/index.php/index/article/view/25426","pdf_url":null,"source":{"id":"https://openalex.org/S4210199200","display_name":"ESTIMASI Journal of Statistics and Its Application","issn_l":"2721-379X","issn":["2721-379X","2721-3803"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/P4310315277","host_organization_name":"Hasanuddin University","host_organization_lineage":["https://openalex.org/P4310315277"],"host_organization_lineage_names":["Hasanuddin University"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2721-3803","raw_type":"info:eu-repo/semantics/article"},"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/W7215996647","https://openalex.org/W7217395995","https://openalex.org/W4412510736","https://openalex.org/W7217403348","https://openalex.org/W2602985908","https://openalex.org/W7163915777","https://openalex.org/W6893122445","https://openalex.org/W7218049018","https://openalex.org/W4387813435","https://openalex.org/W4393206354"],"abstract_inverted_index":{"In":[0,16,68],"the":[1,24,71,86,96,109,127,148,159,164,170,174],"Mixed":[2],"Geographically":[3],"Weighted":[4],"Regression":[5],"(MGWR)":[6],"model,":[7,161],"some":[8,13],"variables":[9,128],"are":[10,14,37,122,135],"local":[11,133],"and":[12,60,124,142],"global.":[15],"MGWR":[17,32,40,74,110,149,160,171],"modeling,":[18],"it":[19,49,105],"is":[20,80,106,156],"often":[21],"found":[22],"that":[23,63,116,129,163],"data":[25,84],"have":[26,117,130],"multicollinearity.":[27],"To":[28],"overcome":[29],"this":[30,69],"problem,":[31],"models":[33],"with":[34,77,112,151],"ridge":[35,78,113,152,167],"regression":[36,79,153,168],"used.":[38],"The":[39,144],"model":[41,75,111,150,172,175],"can":[42,50],"be":[43],"applied":[44,82],"to":[45,55,83,169],"poverty":[46],"cases":[47],"because":[48],"experience":[51,102],"spatial":[52],"heterogeneity":[53],"due":[54],"differences":[56],"in":[57,65,92],"geographical,":[58],"cultural,":[59],"economic":[61],"policies":[62],"vary":[64],"each":[66],"region.":[67],"study,":[70],"estimation":[72],"of":[73,89,99,147,154,166],"parameters":[76],"then":[81],"on":[85,95],"poor":[87,97],"population":[88,98],"South":[90,100],"Sulawesi":[91,101],"2016.":[93],"Data":[94],"multicollinearity,":[103],"so":[104],"solved":[107],"using":[108],"regression.":[114],"Variables":[115],"a":[118,131],"significant":[119,132],"effect":[120,134],"globally":[121],"x3":[123],"x6.":[125],"while":[126],"x2,":[136],"x4,":[137],"x5,":[138],"x7,":[139],"x8,":[140],"x9":[141],"x10.":[143],"AIC":[145],"value":[146],"63.64473":[155],"smaller":[157],"than":[158],"meaning":[162],"addition":[165],"makes":[173],"better":[176],"at":[177],"overcoming":[178],"multicollinearity":[179],"problems.":[180]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2026-10-03T00:00:00"}