{"id":"https://openalex.org/W2017008479","doi":"https://doi.org/10.1214/07-sts242","title":"Boosting Algorithms: Regularization, Prediction and Model Fitting","display_name":"Boosting Algorithms: Regularization, Prediction and Model Fitting","publication_year":2007,"publication_date":"2007-11-01","ids":{"openalex":"https://openalex.org/W2017008479","doi":"https://doi.org/10.1214/07-sts242","mag":"2017008479"},"language":"en","primary_location":{"id":"doi:10.1214/07-sts242","doi":"https://doi.org/10.1214/07-sts242","is_oa":false,"landing_page_url":"https://doi.org/10.1214/07-sts242","pdf_url":null,"source":{"id":"https://openalex.org/S12967704","display_name":"Statistical Science","issn_l":"0883-4237","issn":["0883-4237","2168-8745"],"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","norway-2","poland-200","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310319881","host_organization_name":"Institute of Mathematical Statistics","host_organization_lineage":["https://openalex.org/P4310319881"],"host_organization_lineage_names":["Institute of Mathematical Statistics"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Statistical Science","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/0804.2752","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5033240072","display_name":"Peter B\u00fchlmann","orcid":"https://orcid.org/0000-0002-1782-6015","observed_orcids":["https://orcid.org/0000-0002-1782-6015"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Peter B\u00fchlmann","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Torsten Hothorn","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Torsten Hothorn","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":15.0044,"has_fulltext":true,"cited_by_count":726,"citation_normalized_percentile":{"value":0.99237437,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"22","issue":"4","first_page":"477","last_page":"505"},"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/boosting","display_name":"boosting","score":0.9110000133514404},{"id":"https://openalex.org/keywords/regularization","display_name":"regularization","score":0.9110000133514404},{"id":"https://openalex.org/keywords/feature-selection","display_name":"feature selection","score":0.6669999957084656},{"id":"https://openalex.org/keywords/degrees-of-freedom","display_name":"degrees of freedom","score":0.7480000257492065},{"id":"https://openalex.org/keywords/akaike-information-criterion","display_name":"Akaike information criterion","score":0.9760000109672546},{"id":"https://openalex.org/keywords/bayesian-information-criterion","display_name":"Bayesian information criterion","score":0.9980000257492065},{"id":"https://openalex.org/keywords/generalized-linear-models","display_name":"generalized linear models","score":0.9390000104904175},{"id":"https://openalex.org/keywords/additive-models","display_name":"additive models","score":0.996999979019165},{"id":"https://openalex.org/keywords/survival-analysis","display_name":"survival analysis","score":0.7599999904632568},{"id":"https://openalex.org/keywords/high-dimensional-covariates","display_name":"high-dimensional covariates","score":0.8840000033378601},{"id":"https://openalex.org/keywords/loss-functions","display_name":"loss functions","score":0.6399999856948853},{"id":"https://openalex.org/keywords/model-fitting","display_name":"model fitting","score":0.7409999966621399},{"id":"https://openalex.org/keywords/prediction","display_name":"prediction","score":0.9589999914169312}],"concepts":[{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.779699981212616},{"id":"https://openalex.org/C126674687","wikidata":"https://www.wikidata.org/wiki/Q1662573","display_name":"Akaike information criterion","level":2,"score":0.6718999743461609},{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.6071000099182129},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.5343999862670898},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.5313000082969666},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5159000158309937},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.4672999978065491},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4641999900341034},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.42160001397132874},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.41819998621940613},{"id":"https://openalex.org/C194648359","wikidata":"https://www.wikidata.org/wiki/Q3318054","display_name":"Generalized additive model","level":2,"score":0.4099999964237213},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.400299996137619},{"id":"https://openalex.org/C168136583","wikidata":"https://www.wikidata.org/wiki/Q1988242","display_name":"Bayesian information criterion","level":2,"score":0.390500009059906},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.38929998874664307},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.38089999556541443},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.38019999861717224},{"id":"https://openalex.org/C74127309","wikidata":"https://www.wikidata.org/wiki/Q3455886","display_name":"Nonparametric regression","level":3,"score":0.3686999976634979},{"id":"https://openalex.org/C2984074130","wikidata":"https://www.wikidata.org/wiki/Q73539779","display_name":"R package","level":2,"score":0.3621000051498413},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36079999804496765},{"id":"https://openalex.org/C203223496","wikidata":"https://www.wikidata.org/wiki/Q4681344","display_name":"Additive model","level":2,"score":0.36039999127388},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.35499998927116394},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.3508000075817108},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.335999995470047},{"id":"https://openalex.org/C24574437","wikidata":"https://www.wikidata.org/wiki/Q7135228","display_name":"Parametric model","level":3,"score":0.329800009727478},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.32899999618530273},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3203999996185303},{"id":"https://openalex.org/C27574286","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Variables","level":2,"score":0.3111000061035156},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.3073999881744385},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.296999990940094},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2888999879360199},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.27970001101493835},{"id":"https://openalex.org/C41587187","wikidata":"https://www.wikidata.org/wiki/Q1501882","display_name":"Generalized linear model","level":2,"score":0.27410000562667847},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.27070000767707825},{"id":"https://openalex.org/C203233044","wikidata":"https://www.wikidata.org/wiki/Q5264358","display_name":"Design matrix","level":3,"score":0.257099986076355},{"id":"https://openalex.org/C2776709221","wikidata":"https://www.wikidata.org/wiki/Q6031040","display_name":"Information Criteria","level":3,"score":0.2540000081062317},{"id":"https://openalex.org/C44882253","wikidata":"https://www.wikidata.org/wiki/Q3455882","display_name":"Multivariate adaptive regression splines","level":4,"score":0.25049999356269836}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1214/07-sts242","doi":"https://doi.org/10.1214/07-sts242","is_oa":false,"landing_page_url":"https://doi.org/10.1214/07-sts242","pdf_url":null,"source":{"id":"https://openalex.org/S12967704","display_name":"Statistical Science","issn_l":"0883-4237","issn":["0883-4237","2168-8745"],"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","norway-2","poland-200","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310319881","host_organization_name":"Institute of Mathematical Statistics","host_organization_lineage":["https://openalex.org/P4310319881"],"host_organization_lineage_names":["Institute of Mathematical Statistics"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Statistical Science","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:0804.2752","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/0804.2752","pdf_url":"https://arxiv.org/pdf/0804.2752","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:CULeuclid:euclid.ss/1207580163","doi":null,"is_oa":false,"landing_page_url":"http://projecteuclid.org/euclid.ss/1207580163","pdf_url":null,"source":{"id":"https://openalex.org/S4306400787","display_name":"Project Euclid (Cornell University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Text"},{"id":"doi:10.48550/arxiv.0804.2752","doi":"https://doi.org/10.48550/arxiv.0804.2752","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.0804.2752","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:0804.2752","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/0804.2752","pdf_url":"https://arxiv.org/pdf/0804.2752","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G4860333653","display_name":null,"funder_award_id":"HO 3242/1-3","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"}],"funders":[{"id":"https://openalex.org/F4320320879","display_name":"Deutsche Forschungsgemeinschaft","ror":"https://ror.org/018mejw64"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":78,"referenced_works":["https://openalex.org/W1509718986","https://openalex.org/W1540007258","https://openalex.org/W1555251937","https://openalex.org/W1570060426","https://openalex.org/W1572302037","https://openalex.org/W1608733719","https://openalex.org/W1678356000","https://openalex.org/W1973217014","https://openalex.org/W1975000439","https://openalex.org/W1975846642","https://openalex.org/W1979648751","https://openalex.org/W1982032418","https://openalex.org/W1983590837","https://openalex.org/W1988790447","https://openalex.org/W2007639939","https://openalex.org/W2010824638","https://openalex.org/W2020925091","https://openalex.org/W2024046085","https://openalex.org/W2025266808","https://openalex.org/W2028040032","https://openalex.org/W2034978228","https://openalex.org/W2035683016","https://openalex.org/W2043740082","https://openalex.org/W2045495405","https://openalex.org/W2051922388","https://openalex.org/W2053463056","https://openalex.org/W2057483259","https://openalex.org/W2060771648","https://openalex.org/W2062102668","https://openalex.org/W2063978378","https://openalex.org/W2065748487","https://openalex.org/W2067885219","https://openalex.org/W2068221105","https://openalex.org/W2070094080","https://openalex.org/W2071168995","https://openalex.org/W2076812612","https://openalex.org/W2084812512","https://openalex.org/W2087278921","https://openalex.org/W2088883866","https://openalex.org/W2091178361","https://openalex.org/W2101095383","https://openalex.org/W2102281237","https://openalex.org/W2103038618","https://openalex.org/W2109785413","https://openalex.org/W2110017687","https://openalex.org/W2112076978","https://openalex.org/W2115103545","https://openalex.org/W2120240539","https://openalex.org/W2123998733","https://openalex.org/W2135046866","https://openalex.org/W2135125546","https://openalex.org/W2142399242","https://openalex.org/W2143301524","https://openalex.org/W2151693816","https://openalex.org/W2157875785","https://openalex.org/W2160697532","https://openalex.org/W2162139029","https://openalex.org/W2163150561","https://openalex.org/W2164272351","https://openalex.org/W2166163519","https://openalex.org/W2168020168","https://openalex.org/W2168043231","https://openalex.org/W2168561598","https://openalex.org/W2169577279","https://openalex.org/W2172195373","https://openalex.org/W2502528989","https://openalex.org/W2582743722","https://openalex.org/W2614650776","https://openalex.org/W2911964244","https://openalex.org/W2914629549","https://openalex.org/W2952563653","https://openalex.org/W3004732066","https://openalex.org/W4212883601","https://openalex.org/W4244934246","https://openalex.org/W4246259708","https://openalex.org/W4247843916","https://openalex.org/W4248437541","https://openalex.org/W4399585909"],"related_works":[],"abstract_inverted_index":{"We":[0],"present":[1],"a":[2],"statistical":[3,68],"perspective":[4],"on":[5],"boosting.":[6],"Special":[7],"emphasis":[8],"is":[9,97],"given":[10],"to":[11],"estimating":[12],"potentially":[13],"complex":[14],"parametric":[15],"or":[16,41],"nonparametric":[17],"models,":[18],"including":[19],"generalized":[20],"linear":[21],"and":[22,38,49,93],"additive":[23],"models":[24,29,69],"as":[25,27,58],"well":[26],"regression":[28],"for":[30,47,66,89,100],"survival":[31],"analysis.":[32],"Concepts":[33],"of":[34,36,63,74,103],"degrees":[35],"freedom":[37],"corresponding":[39],"Akaike":[40],"Bayesian":[42],"information":[43],"criteria,":[44],"particularly":[45],"useful":[46],"regularization":[48],"variable":[50,94],"selection":[51],"in":[52],"high-dimensional":[53],"covariate":[54],"spaces,":[55],"are":[56,70],"discussed":[57],"well.":[59],"The":[60],"practical":[61],"aspects":[62],"boosting":[64,105],"procedures":[65],"fitting":[67],"illustrated":[71],"by":[72],"means":[73],"the":[75,101],"dedicated":[76],"open-source":[77],"software":[78],"package":[79,82],"mboost.":[80],"This":[81],"implements":[83],"functions":[84],"which":[85],"can":[86],"be":[87],"used":[88],"model":[90],"fitting,":[91],"prediction":[92],"selection.":[95],"It":[96],"flexible,":[98],"allowing":[99],"implementation":[102],"new":[104],"algorithms":[106],"optimizing":[107],"user-specified":[108],"loss":[109],"functions.":[110]},"counts_by_year":[{"year":2026,"cited_by_count":22},{"year":2025,"cited_by_count":34},{"year":2024,"cited_by_count":50},{"year":2023,"cited_by_count":53},{"year":2022,"cited_by_count":45},{"year":2021,"cited_by_count":57},{"year":2020,"cited_by_count":60},{"year":2019,"cited_by_count":53},{"year":2018,"cited_by_count":42},{"year":2017,"cited_by_count":40},{"year":2016,"cited_by_count":51},{"year":2015,"cited_by_count":36},{"year":2014,"cited_by_count":38},{"year":2013,"cited_by_count":32},{"year":2012,"cited_by_count":34}],"updated_date":"2026-10-14T16:39:01.820549","created_date":"2016-06-24T00:00:00"}
