{"id":"https://openalex.org/W2806923579","doi":"https://doi.org/10.1093/biostatistics/kxx043","title":"High-dimensional multivariate additive regression for uncovering contributing factors to healthcare expenditure","display_name":"High-dimensional multivariate additive regression for uncovering contributing factors to healthcare expenditure","publication_year":2017,"publication_date":"2017-08-23","ids":{"openalex":"https://openalex.org/W2806923579","doi":"https://doi.org/10.1093/biostatistics/kxx043","mag":"2806923579","pmid":"https://pubmed.ncbi.nlm.nih.gov/28968780"},"language":"en","primary_location":{"id":"doi:10.1093/biostatistics/kxx043","doi":"https://doi.org/10.1093/biostatistics/kxx043","is_oa":false,"landing_page_url":"https://doi.org/10.1093/biostatistics/kxx043","pdf_url":null,"source":{"id":"https://openalex.org/S190140075","display_name":"Biostatistics","issn_l":"1465-4644","issn":["1465-4644","1468-4357"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["abdc-a","anvur-class-a","anvur-scientific","cwts-core","dongbi-b","doyens","jufo-2","ki-jl-2","medline","norway-2","poland-100","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310311648","host_organization_name":"Oxford University Press","host_organization_lineage":["https://openalex.org/P4310311648","https://openalex.org/P4310311647"],"host_organization_lineage_names":["Oxford University Press","University of Oxford"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Biostatistics","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["crossref","pubmed"],"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/A5004368441","display_name":"Rodrigue Ngueyep","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4210114115","display_name":"IBM Research - Thomas J. Watson Research Center","ror":"https://ror.org/0265w5591","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Rodrigue Ngueyep","raw_affiliation_strings":["IBM T.J. Watson Research Center, 1101 Kitchawan Road, Yorktown Heights, NY 10598, USA ngueyep@us.ibm.com"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM T.J. Watson Research Center, 1101 Kitchawan Road, Yorktown Heights, NY 10598, USA ngueyep@us.ibm.com","institution_ids":["https://openalex.org/I4210114115"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008942398","display_name":"Nicoleta Serban","orcid":"https://orcid.org/0000-0002-5813-7435","observed_orcids":["https://orcid.org/0000-0002-5813-7435"]},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nicoleta Serban","raw_affiliation_strings":["School of Industrial Systems and Engineering, Georgia Institute of Technology, 755 Ferst Drive, NW, Atlanta, GA 30332, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Industrial Systems and Engineering, Georgia Institute of Technology, 755 Ferst Drive, NW, Atlanta, GA 30332, USA","institution_ids":["https://openalex.org/I130701444"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5004368441"],"corresponding_institution_ids":["https://openalex.org/I4210114115"],"apc_list":{"value":3296,"currency":"USD","value_usd":3296},"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.24782609,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":"19","issue":"3","first_page":"359","last_page":"373"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9994999766349792,"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":0.9994999766349792,"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"}},{"id":"https://openalex.org/T11871","display_name":"Advanced Statistical Methods and Models","score":0.0,"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/high-dimensional-regression","display_name":"high-dimensional regression","score":0.8379999995231628},{"id":"https://openalex.org/keywords/multi-task-regression","display_name":"multi-task regression","score":0.6679999828338623},{"id":"https://openalex.org/keywords/model-selection","display_name":"model selection","score":0.7440000176429749},{"id":"https://openalex.org/keywords/group-sparsity","display_name":"group sparsity","score":0.8240000009536743},{"id":"https://openalex.org/keywords/nonparametric-regression","display_name":"nonparametric regression","score":0.7919999957084656},{"id":"https://openalex.org/keywords/nonlinear-effects","display_name":"nonlinear effects","score":0.8199999928474426},{"id":"https://openalex.org/keywords/health-care-expenditure","display_name":"health care expenditure","score":0.5860000252723694},{"id":"https://openalex.org/keywords/medicaid","display_name":"Medicaid","score":0.847000002861023},{"id":"https://openalex.org/keywords/children","display_name":"children","score":0.597000002861023},{"id":"https://openalex.org/keywords/cancer-data","display_name":"cancer data","score":0.6510000228881836}],"concepts":[{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.6851713061332703},{"id":"https://openalex.org/C2776534028","wikidata":"https://www.wikidata.org/wiki/Q1141363","display_name":"Medicaid","level":3,"score":0.6820384860038757},{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.6625847816467285},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6196762323379517},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.6142534613609314},{"id":"https://openalex.org/C2778738651","wikidata":"https://www.wikidata.org/wiki/Q16546687","display_name":"Novelty","level":2,"score":0.5038723349571228},{"id":"https://openalex.org/C199163554","wikidata":"https://www.wikidata.org/wiki/Q1681619","display_name":"Univariate","level":3,"score":0.5037464499473572},{"id":"https://openalex.org/C160735492","wikidata":"https://www.wikidata.org/wiki/Q31207","display_name":"Health care","level":2,"score":0.48995986580848694},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.48319828510284424},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.48231935501098633},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.46806302666664124},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.45708465576171875},{"id":"https://openalex.org/C74127309","wikidata":"https://www.wikidata.org/wiki/Q3455886","display_name":"Nonparametric regression","level":3,"score":0.45095765590667725},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.44602879881858826},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.4331090450286865},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37047290802001953},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2977376878261566},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15812623500823975},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.1164698600769043},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.0}],"mesh":[{"descriptor_ui":"D002648","descriptor_name":"Child","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D005102","descriptor_name":"Health Expenditures","qualifier_ui":"Q000706","qualifier_name":"statistics & numerical data","is_major_topic":true},{"descriptor_ui":"D005845","descriptor_name":"Georgia","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008484","descriptor_name":"Medicaid","qualifier_ui":"Q000706","qualifier_name":"statistics & numerical data","is_major_topic":true},{"descriptor_ui":"D009657","descriptor_name":"North Carolina","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D012044","descriptor_name":"Regression Analysis","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D014481","descriptor_name":"United States","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D015233","descriptor_name":"Models, Statistical","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D015999","descriptor_name":"Multivariate Analysis","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D056808","descriptor_name":"Biostatistics","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true}],"locations_count":2,"locations":[{"id":"doi:10.1093/biostatistics/kxx043","doi":"https://doi.org/10.1093/biostatistics/kxx043","is_oa":false,"landing_page_url":"https://doi.org/10.1093/biostatistics/kxx043","pdf_url":null,"source":{"id":"https://openalex.org/S190140075","display_name":"Biostatistics","issn_l":"1465-4644","issn":["1465-4644","1468-4357"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["abdc-a","anvur-class-a","anvur-scientific","cwts-core","dongbi-b","doyens","jufo-2","ki-jl-2","medline","norway-2","poland-100","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310311648","host_organization_name":"Oxford University Press","host_organization_lineage":["https://openalex.org/P4310311648","https://openalex.org/P4310311647"],"host_organization_lineage_names":["Oxford University Press","University of Oxford"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Biostatistics","raw_type":"journal-article"},{"id":"pmid:28968780","doi":null,"is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/28968780","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Biostatistics (Oxford, England)","raw_type":"Journal Article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being","score":0.6240000128746033}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1483632999","https://openalex.org/W1727290854","https://openalex.org/W1965650400","https://openalex.org/W1987371344","https://openalex.org/W2003764353","https://openalex.org/W2028496695","https://openalex.org/W2034413656","https://openalex.org/W2045220410","https://openalex.org/W2077659054","https://openalex.org/W2080726496","https://openalex.org/W2092760567","https://openalex.org/W2093097012","https://openalex.org/W2093994886","https://openalex.org/W2099343585","https://openalex.org/W2099696255","https://openalex.org/W2107412135","https://openalex.org/W2125536150","https://openalex.org/W2135046866","https://openalex.org/W2135327149","https://openalex.org/W2136187217","https://openalex.org/W2138019504","https://openalex.org/W2138550913","https://openalex.org/W2166163519","https://openalex.org/W2329959358","https://openalex.org/W2418142167","https://openalex.org/W2593996946","https://openalex.org/W2963498015","https://openalex.org/W6676361318"],"related_works":["https://openalex.org/W7159755138","https://openalex.org/W4386118993","https://openalex.org/W3071516413","https://openalex.org/W7049036401","https://openalex.org/W3032297441","https://openalex.org/W3111281612","https://openalex.org/W4388747802","https://openalex.org/W6946875888","https://openalex.org/W4302599345","https://openalex.org/W4399467601"],"abstract_inverted_index":{"Many":[0],"studies":[1],"in":[2,86,98],"health":[3],"services":[4],"research":[5],"rely":[6],"on":[7,46,155],"regression":[8,35,50],"models":[9],"with":[10,37,189],"a":[11,107,127,162,190],"large":[12,163],"number":[13,164],"of":[14,53,56,74,82,101,110,129,165],"covariates":[15],"or":[16,48,125],"predictors.":[17],"In":[18],"this":[19],"article,":[20],"we":[21,65],"introduce":[22],"novel":[23],"methodology":[24],"to":[25,39,89,122,126,136],"estimate":[26],"and":[27,61,77,94,116,161,173,195],"perform":[28],"model":[29,67],"selection":[30,68],"for":[31,92,143,168],"high-dimensional":[32],"non-parametric":[33],"multivariate":[34],"problems,":[36],"application":[38],"many":[40],"healthcare":[41,141,159],"studies.":[42],"We":[43,104,151,184],"particularly":[44],"focus":[45],"multi-responses":[47],"multi-task":[49],"models.":[51],"Because":[52],"the":[54,57,62,83,99,147,156],"complexity":[55],"dependence":[58],"between":[59,76,93,158],"predictors":[60,118],"multiple":[63],"responses,":[64],"exploit":[66],"approaches":[69],"that":[70,112,119,139],"consider":[71],"various":[72],"level":[73],"groupings":[75],"within":[78,95],"responses.":[79,130],"The":[80],"novelty":[81],"method":[84],"lies":[85],"its":[87],"ability":[88],"account":[90],"simultaneously":[91],"group":[96],"sparsity":[97],"presence":[100],"non-linear":[102],"effects.":[103],"also":[105,185],"propose":[106],"new":[108],"set":[109,194],"algorithms":[111],"can":[113],"identify":[114],"inactive":[115],"active":[117],"are":[120],"common":[121],"all":[123],"responses":[124],"subset":[128],"Our":[131],"modeling":[132],"approach":[133],"is":[134],"applied":[135],"uncover":[137],"factors":[138,167],"impact":[140],"expenditure":[142,160],"children":[144],"insured":[145],"through":[146],"Medicaid":[148,182],"benefits":[149],"program.":[150],"provide":[152],"important":[153],"findings":[154],"association":[157],"well-cited":[166],"two":[169],"neighboring":[170],"states,":[171],"Georgia":[172],"North":[174],"Carolina,":[175],"which":[176],"have":[177],"similar":[178],"demographics":[179],"but":[180],"different":[181],"systems.":[183],"validate":[186],"our":[187],"methods":[188],"benchmark":[191],"cancer":[192],"data":[193,197],"simulated":[196],"examples.":[198]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2}],"updated_date":"2026-10-31T12:09:11.096373","created_date":"2018-06-13T00:00:00"}