{"id":"https://openalex.org/W3133628563","doi":"https://doi.org/10.3390/e23030324","title":"Ensemble Linear Subspace Analysis of High-Dimensional Data","display_name":"Ensemble Linear Subspace Analysis of High-Dimensional Data","publication_year":2021,"publication_date":"2021-03-09","ids":{"openalex":"https://openalex.org/W3133628563","doi":"https://doi.org/10.3390/e23030324","mag":"3133628563","pmid":"https://pubmed.ncbi.nlm.nih.gov/33803346"},"language":"en","primary_location":{"id":"doi:10.3390/e23030324","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e23030324","pdf_url":null,"source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.3390/e23030324","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5026746178","display_name":"Shakeel Ahmed","orcid":"https://orcid.org/0000-0001-6328-7270"},"institutions":[{"id":"https://openalex.org/I186803428","display_name":"Brock University","ror":"https://ror.org/056am2717","country_code":"CA","type":"education","lineage":["https://openalex.org/I186803428"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"S. Ejaz Ahmed","raw_affiliation_strings":["Department of Mathematics and Statistics, Brock University, St. Catharines, ON L2S 3A1, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mathematics and Statistics, Brock University, St. Catharines, ON L2S 3A1, Canada","institution_ids":["https://openalex.org/I186803428"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080157851","display_name":"Saeid Amiri","orcid":"https://orcid.org/0000-0003-2028-092X"},"institutions":[{"id":"https://openalex.org/I45683168","display_name":"Polytechnique Montr\u00e9al","ror":"https://ror.org/05f8d4e86","country_code":"CA","type":"education","lineage":["https://openalex.org/I45683168"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Saeid Amiri","raw_affiliation_strings":["Department of Civil, Geologic and Mining Engineering Polytechnique Montre\u00e1l, Montre\u00e1l, QC H3T 1J4, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Civil, Geologic and Mining Engineering Polytechnique Montre\u00e1l, Montre\u00e1l, QC H3T 1J4, Canada","institution_ids":["https://openalex.org/I45683168"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032417246","display_name":"Kjell A. Doksum","orcid":null},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kjell Doksum","raw_affiliation_strings":["Department of Statistics, University of Wisconsin, Madison, WI 53706, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, University of Wisconsin, Madison, WI 53706, USA","institution_ids":["https://openalex.org/I135310074"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5080157851"],"corresponding_institution_ids":["https://openalex.org/I45683168"],"apc_list":{"value":2000,"currency":"CHF","value_usd":2165},"apc_paid":{"value":2000,"currency":"CHF","value_usd":2165},"fwci":0.9464,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.76299628,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"23","issue":"3","first_page":"324","last_page":"324"},"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/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9868000149726868,"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":0.984499990940094,"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/covariate","display_name":"Covariate","score":0.8591755628585815},{"id":"https://openalex.org/keywords/lasso","display_name":"Lasso (programming language)","score":0.7021946907043457},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.5829473733901978},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.509175717830658},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.5083176493644714},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.47436198592185974},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.4580165445804596},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.45294493436813354},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.43713703751564026},{"id":"https://openalex.org/keywords/linear-model","display_name":"Linear model","score":0.42789506912231445},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4250471889972687},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.42023152112960815},{"id":"https://openalex.org/keywords/ensemble-forecasting","display_name":"Ensemble forecasting","score":0.41922950744628906},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.41354885697364807},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3785133957862854},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.26881730556488037}],"concepts":[{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.8591755628585815},{"id":"https://openalex.org/C37616216","wikidata":"https://www.wikidata.org/wiki/Q3218363","display_name":"Lasso (programming language)","level":2,"score":0.7021946907043457},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.5829473733901978},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.509175717830658},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.5083176493644714},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.47436198592185974},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4580165445804596},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.45294493436813354},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.43713703751564026},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.42789506912231445},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4250471889972687},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.42023152112960815},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.41922950744628906},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.41354885697364807},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3785133957862854},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.26881730556488037},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0}],"mesh":[],"locations_count":6,"locations":[{"id":"doi:10.3390/e23030324","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e23030324","pdf_url":null,"source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},{"id":"pmid:33803346","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/33803346","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":"Entropy (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:doaj.org/article:cdf73eafaef3430bb1357a8a54981abe","is_oa":true,"landing_page_url":"https://doaj.org/article/cdf73eafaef3430bb1357a8a54981abe","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy, Vol 23, Iss 3, p 324 (2021)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/1099-4300/23/3/324/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/e23030324","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"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":"Entropy; Volume 23; Issue 3; Pages: 324","raw_type":"Text"},{"id":"pmh:oai:publications.polymtl.ca:47154","is_oa":false,"landing_page_url":"https://publications.polymtl.ca/47154/","pdf_url":null,"source":{"id":"https://openalex.org/S4306401013","display_name":"PolyPublie (\u00c9cole Polytechnique de Montr\u00e9al)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I45683168","host_organization_name":"Polytechnique Montr\u00e9al","host_organization_lineage":["https://openalex.org/I45683168"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Article de revue"},{"id":"pmh:oai:pubmedcentral.nih.gov:7998555","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7998555","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy (Basel)","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/e23030324","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e23030324","pdf_url":null,"source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.6399999856948853}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1523985187","https://openalex.org/W1971525126","https://openalex.org/W1972278166","https://openalex.org/W2020925091","https://openalex.org/W2063978378","https://openalex.org/W2074682976","https://openalex.org/W2101083207","https://openalex.org/W2109842006","https://openalex.org/W2120875981","https://openalex.org/W2122825543","https://openalex.org/W2127300249","https://openalex.org/W2135046866","https://openalex.org/W2150940164","https://openalex.org/W2488591026","https://openalex.org/W2911964244","https://openalex.org/W2948009788","https://openalex.org/W2966119918","https://openalex.org/W2997644599","https://openalex.org/W3012130715","https://openalex.org/W3100817920","https://openalex.org/W3103935298","https://openalex.org/W3106266785","https://openalex.org/W3116241287","https://openalex.org/W4212883601","https://openalex.org/W4247571494","https://openalex.org/W6682241100"],"related_works":["https://openalex.org/W128985311","https://openalex.org/W2800721711","https://openalex.org/W2794896638","https://openalex.org/W1807784185","https://openalex.org/W4390905871","https://openalex.org/W3202800081","https://openalex.org/W1909207154","https://openalex.org/W3124390867","https://openalex.org/W3101614107","https://openalex.org/W3204228978"],"abstract_inverted_index":{"covariates,":[0],"doing":[1],"statistical":[2],"analysis":[3],"on":[4,103],"each":[5],"subset,":[6],"and":[7,41,51,72,111],"then":[8],"merging":[9],"the":[10,13,30,65,75,87,99,123,126,129,136,145],"results":[11],"from":[12],"subsets.":[14],"We":[15,56,96],"examine":[16,57],"conditions":[17],"under":[18],"which":[19],"penalty":[20,61,104],"methods":[21,105],"such":[22,134],"as":[23],"Lasso":[24,142],"perform":[25],"better":[26],"when":[27,117,125],"used":[28],"in":[29],"ensemble":[31,100,141],"approach":[32,101],"by":[33,70],"computing":[34],"mean":[35],"squared":[36],"prediction":[37],"errors":[38],"for":[39,106],"simulations":[40],"a":[42,79,90],"real":[43,109],"data":[44,110],"example.":[45],"Linear":[46],"models":[47],"with":[48,122],"both":[49],"random":[50],"fixed":[52,93],"designs":[53],"are":[54,119],"considered.":[55],"two":[58],"versions":[59],"of":[60,82,89,92,128,140],"methods:":[62],"one":[63,73],"where":[64,74],"tuning":[66,94],"parameter":[67],"is":[68,78,131,143],"selected":[69],"cross-validation;":[71],"final":[76],"predictor":[77],"trimmed":[80,137],"average":[81,138],"individual":[83],"predictors":[84],"corresponding":[85],"to":[86],"members":[88],"set":[91],"parameters.":[95],"find":[97],"that":[98],"improves":[102],"several":[107],"important":[108],"model":[112,130],"scenarios.":[113],"The":[114],"improvement":[115],"occurs":[116],"covariates":[118],"strongly":[120],"associated":[121],"response,":[124],"complexity":[127],"high.":[132],"In":[133],"cases,":[135],"version":[139],"often":[144],"best":[146],"predictor.":[147]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-04-28T14:05:53.105641","created_date":"2025-10-10T00:00:00"}