{"id":"https://openalex.org/W2168360089","doi":"https://doi.org/10.1002/sim.2833","title":"Variable selection for proportional odds model","display_name":"Variable selection for proportional odds model","publication_year":2007,"publication_date":"2007-02-01","ids":{"openalex":"https://openalex.org/W2168360089","doi":"https://doi.org/10.1002/sim.2833","mag":"2168360089","pmid":"https://pubmed.ncbi.nlm.nih.gov/17266170"},"language":"en","primary_location":{"id":"doi:10.1002/sim.2833","doi":"https://doi.org/10.1002/sim.2833","is_oa":false,"landing_page_url":"https://doi.org/10.1002/sim.2833","pdf_url":null,"source":{"id":"https://openalex.org/S55189604","display_name":"Statistics in Medicine","issn_l":"0277-6715","issn":["0277-6715","1097-0258"],"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/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Statistics in Medicine","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/A5059203764","display_name":"Wenbin Lu","orcid":"https://orcid.org/0000-0002-7320-4755","observed_orcids":["https://orcid.org/0000-0002-7320-4755"]},"institutions":[{"id":"https://openalex.org/I137902535","display_name":"North Carolina State University","ror":"https://ror.org/04tj63d06","country_code":"US","type":"education","lineage":["https://openalex.org/I137902535"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Wenbin Lu","raw_affiliation_strings":["Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA. lu@stat.ncsu.edu","Department of Statistics, North Carolina State University, Raleigh, NC 27695, U.S.A"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA. lu@stat.ncsu.edu","institution_ids":["https://openalex.org/I137902535"]},{"raw_affiliation_string":"Department of Statistics, North Carolina State University, Raleigh, NC 27695, U.S.A","institution_ids":["https://openalex.org/I137902535"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080955712","display_name":"Hao Helen Zhang","orcid":"https://orcid.org/0000-0001-8873-3329","observed_orcids":["https://orcid.org/0000-0001-8873-3329"]},"institutions":[{"id":"https://openalex.org/I137902535","display_name":"North Carolina State University","ror":"https://ror.org/04tj63d06","country_code":"US","type":"education","lineage":["https://openalex.org/I137902535"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hao H. Zhang","raw_affiliation_strings":["Department of Statistics, North Carolina State University, Raleigh, NC 27695, U.S.A"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, North Carolina State University, Raleigh, NC 27695, U.S.A","institution_ids":["https://openalex.org/I137902535"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5059203764"],"corresponding_institution_ids":["https://openalex.org/I137902535"],"apc_list":{"value":5450,"currency":"USD","value_usd":5450},"apc_paid":null,"fwci":3.4291,"has_fulltext":false,"cited_by_count":47,"citation_normalized_percentile":{"value":0.93433433,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":88,"max":99},"biblio":{"volume":"26","issue":"20","first_page":"3771","last_page":"3781"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":1.0,"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"}},{"id":"https://openalex.org/T10968","display_name":"Statistical Distribution Estimation and Applications","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"}},{"id":"https://openalex.org/T13141","display_name":"Statistical Methods and Applications","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/feature-selection","display_name":"feature selection","score":0.9629999995231628},{"id":"https://openalex.org/keywords/proportional-odds-model","display_name":"proportional odds model","score":1.0},{"id":"https://openalex.org/keywords/marginal-likelihood","display_name":"marginal likelihood","score":0.7080000042915344},{"id":"https://openalex.org/keywords/lasso","display_name":"LASSO","score":0.9810000061988831},{"id":"https://openalex.org/keywords/adaptive-lasso","display_name":"adaptive LASSO","score":0.9319999814033508},{"id":"https://openalex.org/keywords/sparse-regression","display_name":"sparse regression","score":0.5410000085830688},{"id":"https://openalex.org/keywords/proportional-hazards-assumption","display_name":"proportional hazards assumption","score":0.6690000295639038},{"id":"https://openalex.org/keywords/ordinal-data","display_name":"ordinal data","score":0.30399999022483826}],"concepts":[{"id":"https://openalex.org/C37616216","wikidata":"https://www.wikidata.org/wiki/Q3218363","display_name":"Lasso (programming language)","level":2,"score":0.8686539530754089},{"id":"https://openalex.org/C143095724","wikidata":"https://www.wikidata.org/wiki/Q515895","display_name":"Odds","level":3,"score":0.6234908103942871},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.602302610874176},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.590162992477417},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5872654914855957},{"id":"https://openalex.org/C6180225","wikidata":"https://www.wikidata.org/wiki/Q3411771","display_name":"Penalty method","level":2,"score":0.5203216075897217},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5031625628471375},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.4822552800178528},{"id":"https://openalex.org/C50382708","wikidata":"https://www.wikidata.org/wiki/Q223218","display_name":"Proportional hazards model","level":2,"score":0.4549699127674103},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.44948798418045044},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.4371410310268402},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3751230537891388},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.36765044927597046},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3511858880519867},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3329404592514038},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.29988589882850647},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.25609657168388367},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.17666175961494446},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.1491997241973877},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0}],"mesh":[{"descriptor_ui":"D000465","descriptor_name":"Algorithms","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":"D016013","descriptor_name":"Likelihood Functions","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016016","descriptor_name":"Proportional Hazards Models","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016019","descriptor_name":"Survival Analysis","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true}],"locations_count":4,"locations":[{"id":"doi:10.1002/sim.2833","doi":"https://doi.org/10.1002/sim.2833","is_oa":false,"landing_page_url":"https://doi.org/10.1002/sim.2833","pdf_url":null,"source":{"id":"https://openalex.org/S55189604","display_name":"Statistics in Medicine","issn_l":"0277-6715","issn":["0277-6715","1097-0258"],"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/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Statistics in Medicine","raw_type":"journal-article"},{"id":"pmid:17266170","doi":null,"is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/17266170","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":"Statistics in medicine","raw_type":"Journal Article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.536.5157","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.536.5157","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www4.stat.ncsu.edu/~lu/research/revise_sm.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.606.8584","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.606.8584","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www4.stat.ncsu.edu/~lu/research/luzhang7.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W171686316","https://openalex.org/W254445687","https://openalex.org/W1263077415","https://openalex.org/W1486911799","https://openalex.org/W1506069954","https://openalex.org/W1540315321","https://openalex.org/W1580788756","https://openalex.org/W1784300750","https://openalex.org/W1969423031","https://openalex.org/W1970070532","https://openalex.org/W2016681503","https://openalex.org/W2020925091","https://openalex.org/W2063978378","https://openalex.org/W2074682976","https://openalex.org/W2079356438","https://openalex.org/W2079724595","https://openalex.org/W2081056762","https://openalex.org/W2102760656","https://openalex.org/W2106161928","https://openalex.org/W2109533003","https://openalex.org/W2129984443","https://openalex.org/W2135046866","https://openalex.org/W2146766088","https://openalex.org/W2146855196","https://openalex.org/W2147534083","https://openalex.org/W2148487094","https://openalex.org/W2149199519","https://openalex.org/W2155628440","https://openalex.org/W2168494806","https://openalex.org/W2317866228","https://openalex.org/W2766138398","https://openalex.org/W2796537448","https://openalex.org/W2796700885","https://openalex.org/W2797502950","https://openalex.org/W2897129259","https://openalex.org/W2905400547","https://openalex.org/W2977606681","https://openalex.org/W3101767848","https://openalex.org/W3147894994","https://openalex.org/W4205969772","https://openalex.org/W4229723287","https://openalex.org/W4285719527","https://openalex.org/W4298876635"],"related_works":["https://openalex.org/W7098372999","https://openalex.org/W2623782087","https://openalex.org/W7100011475","https://openalex.org/W2148487094","https://openalex.org/W2149199519","https://openalex.org/W2129934724","https://openalex.org/W2523030749","https://openalex.org/W7097418729","https://openalex.org/W7006549493","https://openalex.org/W2106161928"],"abstract_inverted_index":{"In":[0,81],"this":[1],"paper":[2],"we":[3],"study":[4],"the":[5,11,21,30,41,47,62,74,77,82,101,124,153,160],"problem":[6],"of":[7,64,69],"variable":[8,65],"selection":[9],"for":[10],"proportional":[12,22,31,42],"odds":[13,43],"model,":[14],"which":[15,55],"is":[16,34],"a":[17,52],"useful":[18],"alternative":[19],"to":[20,39,51,110,113,122,137,156],"hazards":[23,32],"model":[24,44,103],"and":[25,59,76,127,134,149,152],"might":[26],"be":[27,111],"appropriate":[28],"when":[29],"assumption":[33],"not":[35],"satisfied.":[36],"We":[37,115],"propose":[38],"fit":[40],"by":[45],"maximizing":[46],"marginal":[48],"likelihood":[49],"subject":[50],"shrinkage-type":[53],"penalty,":[54,84],"encourages":[56],"sparse":[57],"solutions":[58],"hence":[60],"facilitates":[61],"process":[63],"selection.":[66],"Two":[67],"types":[68],"shrinkage":[70],"penalties":[71],"are":[72,87,96,107],"considered:":[73],"LASSO":[75],"adaptive-LASSO":[78],"(ALASSO)":[79],"penalty.":[80],"ALASSO":[83,154],"different":[85,90],"weights":[86],"imposed":[88],"on":[89],"coefficients":[91],"such":[92],"that":[93,143],"important":[94],"variables":[95],"more":[97,108],"protectively":[98],"retained":[99],"in":[100],"final":[102],"while":[104],"unimportant":[105],"ones":[106],"likely":[109],"shrunk":[112],"zeros.":[114],"further":[116],"provide":[117],"an":[118,135],"efficient":[119],"computation":[120],"algorithm":[121],"implement":[123],"proposed":[125],"methods,":[126],"demonstrate":[128],"their":[129],"performance":[130],"through":[131],"simulation":[132],"studies":[133],"application":[136],"real":[138],"data.":[139],"Numerical":[140],"results":[141],"indicate":[142],"both":[144],"methods":[145],"can":[146],"produce":[147],"accurate":[148],"interpretable":[150],"models,":[151],"tends":[155],"work":[157],"better":[158],"than":[159],"usual":[161],"LASSO.":[162]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":3},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":5},{"year":2012,"cited_by_count":7}],"updated_date":"2026-10-31T12:09:11.096373","created_date":"2016-06-24T00:00:00"}
