{"id":"https://openalex.org/W2994623803","doi":"https://doi.org/10.1109/tencon.2019.8929595","title":"Finding Correlates of Child Mortality in Indonesia Using 3 Regression Methods","display_name":"Finding Correlates of Child Mortality in Indonesia Using 3 Regression Methods","publication_year":2019,"publication_date":"2019-10-01","ids":{"openalex":"https://openalex.org/W2994623803","doi":"https://doi.org/10.1109/tencon.2019.8929595","mag":"2994623803"},"language":"en","primary_location":{"id":"doi:10.1109/tencon.2019.8929595","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tencon.2019.8929595","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON)","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/A5065903666","display_name":"Diana Stojanovic","orcid":null},"institutions":[{"id":"https://openalex.org/I29617571","display_name":"University of Indonesia","ror":"https://ror.org/0116zj450","country_code":"ID","type":"education","lineage":["https://openalex.org/I29617571"]}],"countries":["ID"],"is_corresponding":true,"raw_author_name":"Diana Stojanovic","raw_affiliation_strings":["Lembaga Demografi Faculty of Economis and Business, Universitas Indonesia, Depok, Indonesia"],"affiliations":[{"raw_affiliation_string":"Lembaga Demografi Faculty of Economis and Business, Universitas Indonesia, Depok, Indonesia","institution_ids":["https://openalex.org/I29617571"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072516978","display_name":"Takako Hashimoto","orcid":"https://orcid.org/0000-0002-7762-8336"},"institutions":[{"id":"https://openalex.org/I46943848","display_name":"Chiba University of Commerce","ror":"https://ror.org/02qn0vb48","country_code":"JP","type":"education","lineage":["https://openalex.org/I46943848"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Takako Hashimoto","raw_affiliation_strings":["Institute of Economic Research, Chiba University of Commerce, Chiba, Japan"],"affiliations":[{"raw_affiliation_string":"Institute of Economic Research, Chiba University of Commerce, Chiba, Japan","institution_ids":["https://openalex.org/I46943848"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069362029","display_name":"Yukari Shirota","orcid":null},"institutions":[{"id":"https://openalex.org/I45391821","display_name":"Gakushuin University","ror":"https://ror.org/037s2db26","country_code":"JP","type":"education","lineage":["https://openalex.org/I45391821"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yukari Shirota","raw_affiliation_strings":["Dept. of Management, Faculty of Economics, Gakushuin University, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Dept. of Management, Faculty of Economics, Gakushuin University, Tokyo, Japan","institution_ids":["https://openalex.org/I45391821"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5065903666"],"corresponding_institution_ids":["https://openalex.org/I29617571"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.18125537,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"20","issue":null,"first_page":"1113","last_page":"1117"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10596","display_name":"Child Nutrition and Water Access","score":0.973800003528595,"subfield":{"id":"https://openalex.org/subfields/2916","display_name":"Nutrition and Dietetics"},"field":{"id":"https://openalex.org/fields/29","display_name":"Nursing"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10596","display_name":"Child Nutrition and Water Access","score":0.973800003528595,"subfield":{"id":"https://openalex.org/subfields/2916","display_name":"Nutrition and Dietetics"},"field":{"id":"https://openalex.org/fields/29","display_name":"Nursing"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T12011","display_name":"Insurance, Mortality, Demography, Risk Management","score":0.9315000176429749,"subfield":{"id":"https://openalex.org/subfields/3317","display_name":"Demography"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11886","display_name":"Agricultural risk and resilience","score":0.9115999937057495,"subfield":{"id":"https://openalex.org/subfields/1111","display_name":"Soil Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.6708709001541138},{"id":"https://openalex.org/keywords/poverty","display_name":"Poverty","score":0.6243497133255005},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.6144721508026123},{"id":"https://openalex.org/keywords/total-fertility-rate","display_name":"Total fertility rate","score":0.5603278279304504},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5512557625770569},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.4991786479949951},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4859234392642975},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.4749453365802765},{"id":"https://openalex.org/keywords/bayesian-multivariate-linear-regression","display_name":"Bayesian multivariate linear regression","score":0.4544563293457031},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.45053502917289734},{"id":"https://openalex.org/keywords/demography","display_name":"Demography","score":0.4474027752876282},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.4283793568611145},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.3865915536880493},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.32549524307250977},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.20177391171455383},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.1506282389163971},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.1079174280166626},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.10690507292747498}],"concepts":[{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.6708709001541138},{"id":"https://openalex.org/C189326681","wikidata":"https://www.wikidata.org/wiki/Q10294","display_name":"Poverty","level":2,"score":0.6243497133255005},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.6144721508026123},{"id":"https://openalex.org/C47122089","wikidata":"https://www.wikidata.org/wiki/Q285897","display_name":"Total fertility rate","level":5,"score":0.5603278279304504},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5512557625770569},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.4991786479949951},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4859234392642975},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.4749453365802765},{"id":"https://openalex.org/C64946054","wikidata":"https://www.wikidata.org/wiki/Q4874476","display_name":"Bayesian multivariate linear regression","level":3,"score":0.4544563293457031},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.45053502917289734},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.4474027752876282},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.4283793568611145},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3865915536880493},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.32549524307250977},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.20177391171455383},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.1506282389163971},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.1079174280166626},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.10690507292747498},{"id":"https://openalex.org/C2986817661","wikidata":"https://www.wikidata.org/wiki/Q185698","display_name":"Research methodology","level":3,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0},{"id":"https://openalex.org/C2779076696","wikidata":"https://www.wikidata.org/wiki/Q1280670","display_name":"Family planning","level":4,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tencon.2019.8929595","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tencon.2019.8929595","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/1","display_name":"No poverty","score":0.7900000214576721}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1554944419","https://openalex.org/W2113242816","https://openalex.org/W2171033594","https://openalex.org/W2295598076","https://openalex.org/W2766509441","https://openalex.org/W2770256320","https://openalex.org/W2844392751","https://openalex.org/W2890001704","https://openalex.org/W2898761039","https://openalex.org/W2906377796","https://openalex.org/W2911964244","https://openalex.org/W3102476541","https://openalex.org/W3122442329","https://openalex.org/W6752883890","https://openalex.org/W6884901743"],"related_works":["https://openalex.org/W2967733078","https://openalex.org/W3204430031","https://openalex.org/W3137904399","https://openalex.org/W4310492845","https://openalex.org/W4386690025","https://openalex.org/W2885778889","https://openalex.org/W4310224730","https://openalex.org/W2766514146","https://openalex.org/W4289703016","https://openalex.org/W2885516856"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3,47],"contrast":[4],"three":[5,45,100,139],"regression":[6,54,155],"methods":[7,46,101,156],"on":[8,130],"the":[9,13,27,39,79,88,105,121,134,138,144,167,170,174],"example":[10],"of":[11,15,35,82,107,109,116,132],"finding":[12],"correlates":[14],"child":[16],"mortality":[17,80],"rates":[18,81],"(under5mort)":[19],"by":[20],"province":[21],"in":[22,104,165],"Indonesia.":[23],"Factors":[24],"examined":[25],"include":[26],"average":[28,33],"high-school":[29,89],"enrollment":[30,90],"rate":[31,42,91],"(enrollment),":[32],"level":[34],"poverty":[36,73,147],"(poverty),":[37],"and":[38,62,72,118,143,153,173],"total":[40],"fertility":[41],"(TFR).":[43],"The":[44],"compared":[48],"are:":[49],"1)":[50],"traditional":[51],"multiple":[52],"linear":[53],"(MLR),":[55],"2)":[56],"eXtreme":[57],"Gradient":[58],"Boosting":[59],"(XGBoost)":[60],"algorithm,":[61],"3)":[63],"Random":[64],"Forest":[65],"(RF)":[66],"algorithm.":[67],"We":[68,149],"find":[69],"that":[70,151],"TFR":[71,142],"show":[74],"statistically":[75],"significant":[76],"relationship":[77,168],"with":[78],"children":[83],"under":[84],"age":[85],"5,":[86],"while":[87],"does":[92],"not.":[93],"Results":[94],"are":[95],"qualitatively":[96],"same":[97],"for":[98,125],"all":[99],"but":[102],"differ":[103],"value":[106],"coefficient":[108,115],"determination":[110,117],"R-squared.":[111],"XGBoost":[112,152],"has":[113],"highest":[114],"is":[119,141,146,176],"thus":[120],"best":[122],"fitting":[123],"model":[124],"our":[126],"data":[127],"samples.":[128],"Based":[129],"order":[131],"features,":[133],"most":[135],"important":[136],"among":[137],"factors":[140],"next":[145],"level.":[148],"propose":[150],"RF":[154],"could":[157],"be":[158],"successfully":[159],"applied":[160],"to":[161],"other":[162],"demographic":[163],"analyses":[164],"which":[166],"between":[169],"feature":[171],"variables":[172],"predictor":[175],"not":[177],"linear.":[178]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
