{"id":"https://openalex.org/W4210526861","doi":"https://doi.org/10.1080/10618600.2022.2035231","title":"Sequential Learning of Regression Models by Penalized Estimation","display_name":"Sequential Learning of Regression Models by Penalized Estimation","publication_year":2022,"publication_date":"2022-01-31","ids":{"openalex":"https://openalex.org/W4210526861","doi":"https://doi.org/10.1080/10618600.2022.2035231"},"language":"en","primary_location":{"id":"doi:10.1080/10618600.2022.2035231","is_oa":false,"landing_page_url":"https://doi.org/10.1080/10618600.2022.2035231","pdf_url":null,"source":{"id":"https://openalex.org/S76159266","display_name":"Journal of Computational and Graphical Statistics","issn_l":"1061-8600","issn":["1061-8600","1537-2715"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computational and Graphical Statistics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://pure.amsterdamumc.nl/files/157514214/Sequential-learning-of-regression-models-by-penalized-estimation.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5090122358","display_name":"Wessel N. van Wieringen","orcid":"https://orcid.org/0000-0002-5100-9123"},"institutions":[{"id":"https://openalex.org/I865915315","display_name":"Vrije Universiteit Amsterdam","ror":"https://ror.org/008xxew50","country_code":"NL","type":"education","lineage":["https://openalex.org/I865915315"]},{"id":"https://openalex.org/I4210151833","display_name":"Amsterdam University Medical Centers","ror":"https://ror.org/05grdyy37","country_code":"NL","type":"funder","lineage":["https://openalex.org/I4210151833"]},{"id":"https://openalex.org/I4210148356","display_name":"GGD Amsterdam","ror":"https://ror.org/04gbbq803","country_code":"NL","type":"healthcare","lineage":["https://openalex.org/I4210088893","https://openalex.org/I4210148356"]}],"countries":["NL"],"is_corresponding":true,"raw_author_name":"Wessel N. van Wieringen","raw_affiliation_strings":["Department of Epidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam UMC, Amsterdam, The Netherlands;;","Department of Mathematics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands;;"],"affiliations":[{"raw_affiliation_string":"Department of Epidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam UMC, Amsterdam, The Netherlands;;","institution_ids":["https://openalex.org/I4210148356","https://openalex.org/I4210151833"]},{"raw_affiliation_string":"Department of Mathematics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands;;","institution_ids":["https://openalex.org/I865915315"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011534196","display_name":"Harald Binder","orcid":"https://orcid.org/0000-0002-5666-8662"},"institutions":[{"id":"https://openalex.org/I161046081","display_name":"University of Freiburg","ror":"https://ror.org/0245cg223","country_code":"DE","type":"education","lineage":["https://openalex.org/I161046081"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Harald Binder","raw_affiliation_strings":["Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany"],"affiliations":[{"raw_affiliation_string":"Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany","institution_ids":["https://openalex.org/I161046081"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5090122358"],"corresponding_institution_ids":["https://openalex.org/I4210148356","https://openalex.org/I4210151833","https://openalex.org/I865915315"],"apc_list":null,"apc_paid":null,"fwci":1.7267,"has_fulltext":true,"cited_by_count":7,"citation_normalized_percentile":{"value":0.84532072,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"31","issue":"3","first_page":"877","last_page":"886"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9976999759674072,"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":0.9976999759674072,"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/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.9900000095367432,"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/T11443","display_name":"Advanced Statistical Process Monitoring","score":0.9894000291824341,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"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/estimator","display_name":"Estimator","score":0.6998689770698547},{"id":"https://openalex.org/keywords/covariate","display_name":"Covariate","score":0.6382414102554321},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5016300678253174},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.467123806476593},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4663239121437073},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.4393289387226105},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4239095449447632},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.416594535112381},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.4045370817184448},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3628005385398865}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6998689770698547},{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.6382414102554321},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5016300678253174},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.467123806476593},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4663239121437073},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.4393289387226105},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4239095449447632},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.416594535112381},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4045370817184448},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3628005385398865},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1080/10618600.2022.2035231","is_oa":false,"landing_page_url":"https://doi.org/10.1080/10618600.2022.2035231","pdf_url":null,"source":{"id":"https://openalex.org/S76159266","display_name":"Journal of Computational and Graphical Statistics","issn_l":"1061-8600","issn":["1061-8600","1537-2715"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computational and Graphical Statistics","raw_type":"journal-article"},{"id":"pmh:oai:pure.amsterdamumc.nl:openaire/5ccb3b93-d4f9-43bf-af35-0f689b116797","is_oa":true,"landing_page_url":"https://pure.amsterdamumc.nl/en/publications/5ccb3b93-d4f9-43bf-af35-0f689b116797","pdf_url":"https://pure.amsterdamumc.nl/files/157514214/Sequential-learning-of-regression-models-by-penalized-estimation.pdf","source":{"id":"https://openalex.org/S7407055222","display_name":"Pure Amsterdam UMC","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"van Wieringen, W N & Binder, H 2022, 'Sequential Learning of Regression Models by Penalized Estimation', Journal of Computational and Graphical Statistics, vol. 31, no. 3, pp. 877-886. https://doi.org/10.1080/10618600.2022.2035231","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:vumc:oai:pure.atira.dk:publications/ac51a1d3-7ed2-4bec-b671-26dccc50c2bc","is_oa":true,"landing_page_url":"https://research.vumc.nl/en/publications/ac51a1d3-7ed2-4bec-b671-26dccc50c2bc","pdf_url":null,"source":{"id":"https://openalex.org/S4306401843","display_name":"Data Archiving and Networked Services (DANS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1322597698","host_organization_name":"Royal Netherlands Academy of Arts and Sciences","host_organization_lineage":["https://openalex.org/I1322597698"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Journal of Computational and Graphical Statistics, 31(3), 877 - 886. Taylor and Francis Ltd.","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"pmh:oai:pure.amsterdamumc.nl:openaire/5ccb3b93-d4f9-43bf-af35-0f689b116797","is_oa":true,"landing_page_url":"https://pure.amsterdamumc.nl/en/publications/5ccb3b93-d4f9-43bf-af35-0f689b116797","pdf_url":"https://pure.amsterdamumc.nl/files/157514214/Sequential-learning-of-regression-models-by-penalized-estimation.pdf","source":{"id":"https://openalex.org/S7407055222","display_name":"Pure Amsterdam UMC","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"van Wieringen, W N & Binder, H 2022, 'Sequential Learning of Regression Models by Penalized Estimation', Journal of Computational and Graphical Statistics, vol. 31, no. 3, pp. 877-886. https://doi.org/10.1080/10618600.2022.2035231","raw_type":"info:eu-repo/semantics/publishedVersion"},"sustainable_development_goals":[{"score":0.5299999713897705,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4210526861.pdf"},"referenced_works_count":25,"referenced_works":["https://openalex.org/W1460189015","https://openalex.org/W1480376833","https://openalex.org/W1601795611","https://openalex.org/W1967556010","https://openalex.org/W1981552604","https://openalex.org/W1988520084","https://openalex.org/W1997692683","https://openalex.org/W2005650084","https://openalex.org/W2033623507","https://openalex.org/W2048424346","https://openalex.org/W2052400833","https://openalex.org/W2106393550","https://openalex.org/W2139766869","https://openalex.org/W2165698076","https://openalex.org/W2758219826","https://openalex.org/W2961195446","https://openalex.org/W3000686922","https://openalex.org/W3013742260","https://openalex.org/W3037970096","https://openalex.org/W3104887532","https://openalex.org/W4230076583","https://openalex.org/W4234698323","https://openalex.org/W4244359411","https://openalex.org/W4292022450","https://openalex.org/W4297873013"],"related_works":["https://openalex.org/W2112143119","https://openalex.org/W4289356671","https://openalex.org/W2389155397","https://openalex.org/W2165884543","https://openalex.org/W2312753042","https://openalex.org/W3186837933","https://openalex.org/W1969346022","https://openalex.org/W2368989808","https://openalex.org/W2034959125","https://openalex.org/W2355687852"],"abstract_inverted_index":{"When":[0],"data":[1,36,81,189],"arrive":[2,190],"in":[3,112,191],"a":[4,26,70,99,205],"sequence":[5,100],"of":[6,101,143,145,164,204],"two":[7],"or":[8],"more":[9],"datasets,":[10],"modeling":[11,30,177],"on":[12,166,183],"the":[13,67,79,104,141,146,150,160,188,197,202],"most":[14],"recent":[15],"dataset":[16],"should":[17],"take":[18],"previous":[19,35,80],"datasets":[20,102],"into":[21],"account.":[22],"We":[23],"specifically":[24],"investigate":[25],"strategy":[27],"for":[28,48,210],"regression":[29,117,176],"when":[31,87],"parameter":[32,76,118,130],"estimates":[33],"from":[34,78,92,153],"can":[37],"be":[38,46,55,123],"used":[39],"as":[40,83],"anchoring":[41],"points,":[42],"yet":[43],"may":[44],"not":[45],"available":[47,214],"all":[49],"parameters,":[50],"thus,":[51],"covariance":[52],"information":[53],"cannot":[54],"reused.":[56],"A":[57],"procedure":[58],"that":[59],"updates":[60],"through":[61,133],"targeted":[62],"penalized":[63],"estimation,":[64],"which":[65],"shrinks":[66],"estimator":[68,119,148],"toward":[69,149],"nonzero":[71,85],"value,":[72],"is":[73,90,120,131,172,181,199],"presented.":[74],"The":[75,114,128,138,155,169],"estimate":[77],"serves":[82],"this":[84,211],"value":[86],"an":[88,184],"update":[89],"sought":[91],"novel":[93,206],"data.":[94,168],"This":[95],"naturally":[96],"extends":[97],"to":[98,122,158,174],"with":[103,193,201],"same":[105],"response,":[106],"but":[107],"potentially":[108],"only":[109],"partial":[110],"overlap":[111],"covariates.":[113],"iteratively":[115],"updated":[116,147],"shown":[121],"asymptotically":[124],"unbiased":[125],"and":[126,196],"consistent.":[127],"penalty":[129],"chosen":[132],"constrained":[134],"cross-validated":[135],"log-likelihood":[136],"optimization.":[137],"constraint":[139],"bounds":[140],"amount":[142],"shrinkage":[144],"current":[151],"one":[152],"below.":[154],"bound":[156],"aims":[157],"preserve":[159],"(updated)":[161],"estimator\u2019s":[162],"goodness":[163],"fit":[165],"all-but-the-novel":[167],"proposed":[170],"approach":[171],"compared":[173],"other":[175],"procedures.":[178],"Finally,":[179],"it":[180],"illustrated":[182],"epidemiological":[185],"study":[186],"where":[187],"batches":[192],"different":[194],"covariate-availability":[195],"model":[198],"refitted":[200],"availability":[203],"batch.":[207],"Supplementary":[208],"materials":[209],"article":[212],"are":[213],"online.":[215]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2}],"updated_date":"2026-03-17T17:19:04.345684","created_date":"2025-10-10T00:00:00"}