{"id":"https://openalex.org/W4410727829","doi":"https://doi.org/10.1007/s00180-025-01635-0","title":"A powerful penalized multinomial logistic regression approach","display_name":"A powerful penalized multinomial logistic regression approach","publication_year":2025,"publication_date":"2025-05-25","ids":{"openalex":"https://openalex.org/W4410727829","doi":"https://doi.org/10.1007/s00180-025-01635-0","pmid":"https://pubmed.ncbi.nlm.nih.gov/41141894"},"language":"en","primary_location":{"id":"doi:10.1007/s00180-025-01635-0","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00180-025-01635-0","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00180-025-01635-0.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","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computational Statistics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s00180-025-01635-0.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5020904196","display_name":"Cornelia Fuetterer","orcid":"https://orcid.org/0000-0002-8354-6660"},"institutions":[{"id":"https://openalex.org/I2802619606","display_name":"TUM Klinikum","ror":"https://ror.org/04jc43x05","country_code":"DE","type":"healthcare","lineage":["https://openalex.org/I2802619606","https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Cornelia Fuetterer","raw_affiliation_strings":["Institute of AI and Informatics in Medicine, School of Medicine and Health, TUM University Hospital, Technical University of Munich (TUM), Ismaninger Stra\u00dfe 22, 81675 Munich, Germany","Institute of AI and Informatics in Medicine, School of Medicine and Health, TUM University Hospital, Technical University of Munich (TUM), Ismaninger Stra\u00dfe 22, 81675, Munich, Germany"],"raw_orcid":"https://orcid.org/0000-0002-8354-6660","affiliations":[{"raw_affiliation_string":"Institute of AI and Informatics in Medicine, School of Medicine and Health, TUM University Hospital, Technical University of Munich (TUM), Ismaninger Stra\u00dfe 22, 81675 Munich, Germany","institution_ids":["https://openalex.org/I2802619606"]},{"raw_affiliation_string":"Institute of AI and Informatics in Medicine, School of Medicine and Health, TUM University Hospital, Technical University of Munich (TUM), Ismaninger Stra\u00dfe 22, 81675, Munich, Germany","institution_ids":["https://openalex.org/I2802619606"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085463590","display_name":"Malte Nalenz","orcid":"https://orcid.org/0000-0003-3439-4469"},"institutions":[{"id":"https://openalex.org/I8204097","display_name":"Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen","ror":"https://ror.org/05591te55","country_code":"DE","type":"education","lineage":["https://openalex.org/I8204097"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Malte Nalenz","raw_affiliation_strings":["Department of Statistics, Ludwig-Maximilians-University Munich, Ludwigstra\u00dfe 33, 80539 Munich, Germany","Department of Statistics, Ludwig-Maximilians-University Munich, Ludwigstra\u00dfe 33, 80539, Munich, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, Ludwig-Maximilians-University Munich, Ludwigstra\u00dfe 33, 80539 Munich, Germany","institution_ids":["https://openalex.org/I8204097"]},{"raw_affiliation_string":"Department of Statistics, Ludwig-Maximilians-University Munich, Ludwigstra\u00dfe 33, 80539, Munich, Germany","institution_ids":["https://openalex.org/I8204097"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028359392","display_name":"Thomas Augustin","orcid":"https://orcid.org/0000-0002-1854-6226"},"institutions":[{"id":"https://openalex.org/I8204097","display_name":"Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen","ror":"https://ror.org/05591te55","country_code":"DE","type":"education","lineage":["https://openalex.org/I8204097"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Thomas Augustin","raw_affiliation_strings":["Department of Statistics, Ludwig-Maximilians-University Munich, Ludwigstra\u00dfe 33, 80539 Munich, Germany","Department of Statistics, Ludwig-Maximilians-University Munich, Ludwigstra\u00dfe 33, 80539, Munich, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, Ludwig-Maximilians-University Munich, Ludwigstra\u00dfe 33, 80539 Munich, Germany","institution_ids":["https://openalex.org/I8204097"]},{"raw_affiliation_string":"Department of Statistics, Ludwig-Maximilians-University Munich, Ludwigstra\u00dfe 33, 80539, Munich, Germany","institution_ids":["https://openalex.org/I8204097"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037297600","display_name":"Ruth M. Pfeiffer","orcid":"https://orcid.org/0000-0001-7791-2698"},"institutions":[{"id":"https://openalex.org/I4210140884","display_name":"National Cancer Institute","ror":"https://ror.org/040gcmg81","country_code":"US","type":"government","lineage":["https://openalex.org/I1299022934","https://openalex.org/I1299303238","https://openalex.org/I4210140884"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Ruth M. Pfeiffer","raw_affiliation_strings":["Biostatistics Branch, National Cancer Institute, 9609 Medical Center Drive, Bethesda, MD 20892 USA","Biostatistics Branch, National Cancer Institute, 9609 Medical Center Drive, Bethesda, MD, 20892, USA"],"raw_orcid":"https://orcid.org/0000-0001-7791-2698","affiliations":[{"raw_affiliation_string":"Biostatistics Branch, National Cancer Institute, 9609 Medical Center Drive, Bethesda, MD 20892 USA","institution_ids":["https://openalex.org/I4210140884"]},{"raw_affiliation_string":"Biostatistics Branch, National Cancer Institute, 9609 Medical Center Drive, Bethesda, MD, 20892, USA","institution_ids":["https://openalex.org/I4210140884"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5020904196","https://openalex.org/A5037297600"],"corresponding_institution_ids":["https://openalex.org/I2802619606","https://openalex.org/I4210140884"],"apc_list":{"value":2490,"currency":"EUR","value_usd":3090},"apc_paid":{"value":2490,"currency":"EUR","value_usd":3090},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.10714587,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"40","issue":"8","first_page":"4565","last_page":"4587"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9922000169754028,"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":0.9922000169754028,"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/T12135","display_name":"Fuzzy Systems and Optimization","score":0.9754999876022339,"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/T11798","display_name":"Optimal Experimental Design Methods","score":0.9668999910354614,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/multinomial-logistic-regression","display_name":"Multinomial logistic regression","score":0.8233144283294678},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.6644605398178101},{"id":"https://openalex.org/keywords/multinomial-distribution","display_name":"Multinomial distribution","score":0.5380204916000366},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.47765791416168213},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4753789007663727},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.4545705318450928},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.42994585633277893},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.37246841192245483},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.33809998631477356}],"concepts":[{"id":"https://openalex.org/C117568660","wikidata":"https://www.wikidata.org/wiki/Q1650843","display_name":"Multinomial logistic regression","level":2,"score":0.8233144283294678},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.6644605398178101},{"id":"https://openalex.org/C192065140","wikidata":"https://www.wikidata.org/wiki/Q1147928","display_name":"Multinomial distribution","level":2,"score":0.5380204916000366},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.47765791416168213},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4753789007663727},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.4545705318450928},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.42994585633277893},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.37246841192245483},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33809998631477356}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1007/s00180-025-01635-0","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00180-025-01635-0","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00180-025-01635-0.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","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computational Statistics","raw_type":"journal-article"},{"id":"pmid:41141894","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/41141894","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,"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":"Computational statistics","raw_type":null},{"id":"pmh:oai:europepmc.org:11359874","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/12552268","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC12552268/pdf/180_2025_Article_1635.pdf","source":{"id":"https://openalex.org/S4306400806","display_name":"Europe PMC (PubMed Central)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1303153112","host_organization_name":"European Bioinformatics Institute","host_organization_lineage":["https://openalex.org/I1303153112"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Text"}],"best_oa_location":{"id":"doi:10.1007/s00180-025-01635-0","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00180-025-01635-0","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00180-025-01635-0.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","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computational Statistics","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320320868","display_name":"Boehringer Ingelheim Fonds","ror":"https://ror.org/00dkye506"},{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4410727829.pdf","grobid_xml":"https://content.openalex.org/works/W4410727829.grobid-xml"},"referenced_works_count":36,"referenced_works":["https://openalex.org/W1513618424","https://openalex.org/W1965125844","https://openalex.org/W1987971958","https://openalex.org/W2010653277","https://openalex.org/W2018998004","https://openalex.org/W2020925091","https://openalex.org/W2023887100","https://openalex.org/W2042063151","https://openalex.org/W2051224630","https://openalex.org/W2074682976","https://openalex.org/W2078864580","https://openalex.org/W2096801452","https://openalex.org/W2122825543","https://openalex.org/W2135046866","https://openalex.org/W2140258573","https://openalex.org/W2145473366","https://openalex.org/W2149144946","https://openalex.org/W2149573463","https://openalex.org/W2150579376","https://openalex.org/W2152734820","https://openalex.org/W2164092415","https://openalex.org/W2171033594","https://openalex.org/W2272182811","https://openalex.org/W2337733832","https://openalex.org/W2792647356","https://openalex.org/W2949078246","https://openalex.org/W3045153201","https://openalex.org/W3098306969","https://openalex.org/W3101044299","https://openalex.org/W3105513009","https://openalex.org/W4200219333","https://openalex.org/W4205421521","https://openalex.org/W4229873072","https://openalex.org/W4256202811","https://openalex.org/W4294541781","https://openalex.org/W4312328572"],"related_works":["https://openalex.org/W3033697969","https://openalex.org/W2104977651","https://openalex.org/W2369306031","https://openalex.org/W4248368593","https://openalex.org/W4246416652","https://openalex.org/W2418252711","https://openalex.org/W3098841390","https://openalex.org/W2784774275","https://openalex.org/W1917858188","https://openalex.org/W3123107177"],"abstract_inverted_index":{"Abstract":[0],"Penalized":[1],"regression":[2,27,123],"methods":[3,134,237],"that":[4,43,58,107],"shrink":[5],"model":[6],"coefficients":[7],"are":[8],"popular":[9],"approaches":[10,256],"to":[11,75,249],"improve":[12],"prediction":[13],"and":[14,49,62,70,84,97,131,149,229],"for":[15,29,33,78,88,207,243],"variable":[16],"selection":[17],"in":[18,101,135,165,174,247,257],"high-dimensional":[19,175,250,259],"settings.":[20,176,210],"We":[21,82,125,252],"present":[22],"a":[23,37,72],"penalized":[24,68],"(or":[25],"regularized)":[26],"approach":[28,158],"multinomial":[30],"logistic":[31],"models":[32,168],"categorical":[34,245],"outcomes":[35,246],"with":[36,121,137,159],"novel":[38,105],"adaptive":[39],"L1-type":[40],"penalty":[41],"term,":[42],"incorporates":[44],"weights":[45,162],"based":[46,161],"on":[47],"intra-":[48],"inter-outcome":[50],"category":[51,65],"distances":[52,66],"of":[53,93,118,129,140,144,147,181,235],"each":[54],"predictor.":[55],"A":[56],"predictor":[57,150],"has":[59,71],"large":[60],"between-":[61],"small":[63],"within-outcome":[64],"is":[67],"less":[69],"higher":[73],"likelihood":[74],"be":[76],"selected":[77],"the":[79,110,127,156,178,188,196,230],"final":[80],"model.":[81],"propose":[83],"study":[85],"three":[86],"measures":[87],"weight":[89],"calculation:":[90],"an":[91],"analysis":[92],"variance":[94],"(ANOVA)-based":[95],"measure":[96],"two":[98],"indices":[99],"used":[100],"clustering":[102],"approaches.":[103],"Our":[104],"approach,":[106],"we":[108,240],"term":[109],"discriminative":[111],"power":[112],"lasso":[113],"(DP-lasso),":[114],"thus":[115],"combines":[116],"elements":[117],"marginal":[119],"screening":[120],"regularized":[122],"methods.":[124],"studied":[126],"performance":[128],"DP-lasso":[130,157],"other":[132,170],"published":[133],"simulations":[136],"varying":[138],"numbers":[139,143],"outcome":[141],"categories,":[142],"predictors,":[145,155],"strengths":[146],"associations":[148],"correlation":[151],"structures.":[152],"For":[153],"correlated":[154],"ANOVA":[160],"(DPan)":[163],"resulted":[164],"much":[166,185],"sparser":[167],"than":[169,187],"regularization":[171],"approaches,":[172],"especially":[173],"When":[177],"number":[179],"p":[180],"(correlated)":[182],"predictors":[183],"was":[184],"larger":[186],"available":[189],"sample":[190],"size":[191],"N":[192],",":[193,222],"DPan":[194,223,242],"had":[195,224],"highest":[197],"true":[198,226],"positive":[199,205,227,233],"rate":[200],"while":[201],"maintaining":[202],"low":[203],"false":[204,232],"rates":[206,228,234],"all":[208,236,255],"simulation":[209],"Similarly,":[211],"when":[212],"$${p&lt;N}$$":[213],"<mml:math":[214],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\">":[215],"<mml:mrow>":[216],"<mml:mi>p</mml:mi>":[217],"<mml:mo>&lt;</mml:mo>":[218],"<mml:mi>N</mml:mi>":[219],"</mml:mrow>":[220],"</mml:math>":[221],"high":[225],"lowest":[231],"studied.":[238],"Thus":[239],"recommend":[241],"analysing":[244],"relation":[248],"predictors.":[251],"further":[253],"illustrate":[254],"ultra":[258],"settings,":[260],"using":[261],"several":[262],"single-cell":[263],"RNA-sequencing":[264],"datasets.":[265]},"counts_by_year":[],"updated_date":"2026-05-16T08:24:45.110214","created_date":"2025-10-10T00:00:00"}
