{"id":"https://openalex.org/W2241444664","doi":"https://doi.org/10.1109/camsap.2015.7383817","title":"Quantifying uncertainty in variable selection with arbitrary matrices","display_name":"Quantifying uncertainty in variable selection with arbitrary matrices","publication_year":2015,"publication_date":"2015-12-01","ids":{"openalex":"https://openalex.org/W2241444664","doi":"https://doi.org/10.1109/camsap.2015.7383817","mag":"2241444664"},"language":"en","primary_location":{"id":"doi:10.1109/camsap.2015.7383817","is_oa":false,"landing_page_url":"https://doi.org/10.1109/camsap.2015.7383817","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"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/A5065189850","display_name":"Willem van den Boom","orcid":"https://orcid.org/0000-0002-1777-3857"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Willem van den Boom","raw_affiliation_strings":["Department of Statistical Science, Duke University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistical Science, Duke University","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002768909","display_name":"David B. Dunson","orcid":"https://orcid.org/0000-0003-4942-1597"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"David Dunson","raw_affiliation_strings":["Department of Statistical Science, Duke University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistical Science, Duke University","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084396713","display_name":"Galen Reeves","orcid":"https://orcid.org/0000-0003-4230-0688"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Galen Reeves","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Duke University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Duke University","institution_ids":["https://openalex.org/I170897317"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I170897317"],"apc_list":null,"apc_paid":null,"fwci":1.3403,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.80455166,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"15","issue":null,"first_page":"385","last_page":"388"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10928","display_name":"Probabilistic and Robust Engineering Design","score":0.9983999729156494,"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"}},{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9951000213623047,"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/computer-science","display_name":"Computer science","score":0.6115609407424927},{"id":"https://openalex.org/keywords/random-variable","display_name":"Random variable","score":0.5196941494941711},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.49163514375686646},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.49029281735420227},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.48072555661201477},{"id":"https://openalex.org/keywords/posterior-probability","display_name":"Posterior probability","score":0.47740426659584045},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.43679049611091614},{"id":"https://openalex.org/keywords/variable","display_name":"Variable (mathematics)","score":0.4187544882297516},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.4157376289367676},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3909882605075836},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3340491056442261},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3019527196884155},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.28618741035461426},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.25747400522232056},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.18022358417510986}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6115609407424927},{"id":"https://openalex.org/C122123141","wikidata":"https://www.wikidata.org/wiki/Q176623","display_name":"Random variable","level":2,"score":0.5196941494941711},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.49163514375686646},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.49029281735420227},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.48072555661201477},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.47740426659584045},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.43679049611091614},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.4187544882297516},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.4157376289367676},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3909882605075836},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3340491056442261},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3019527196884155},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.28618741035461426},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.25747400522232056},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.18022358417510986},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/camsap.2015.7383817","is_oa":false,"landing_page_url":"https://doi.org/10.1109/camsap.2015.7383817","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.8199999928474426,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W171292237","https://openalex.org/W340056678","https://openalex.org/W603679014","https://openalex.org/W1855388283","https://openalex.org/W1941452196","https://openalex.org/W1987772002","https://openalex.org/W1999351024","https://openalex.org/W1999974018","https://openalex.org/W2007069447","https://openalex.org/W2017696952","https://openalex.org/W2044797198","https://openalex.org/W2045381005","https://openalex.org/W2049228615","https://openalex.org/W2059703530","https://openalex.org/W2069119359","https://openalex.org/W2077621940","https://openalex.org/W2078411132","https://openalex.org/W2082029531","https://openalex.org/W2085493178","https://openalex.org/W2090070058","https://openalex.org/W2091482891","https://openalex.org/W2099170797","https://openalex.org/W2108562800","https://openalex.org/W2128235479","https://openalex.org/W2168537135","https://openalex.org/W2170118047","https://openalex.org/W2963278901","https://openalex.org/W3014551858","https://openalex.org/W3099550161","https://openalex.org/W4235256018","https://openalex.org/W4246858143","https://openalex.org/W4247571494","https://openalex.org/W4285719527","https://openalex.org/W6606997615","https://openalex.org/W6639129518","https://openalex.org/W6676241399","https://openalex.org/W6679265385","https://openalex.org/W6775640718"],"related_works":["https://openalex.org/W2494523064","https://openalex.org/W2943623134","https://openalex.org/W2588219639","https://openalex.org/W2030292806","https://openalex.org/W2032094637","https://openalex.org/W2040227828","https://openalex.org/W2060045818","https://openalex.org/W2131935101","https://openalex.org/W856257623","https://openalex.org/W2892315154"],"abstract_inverted_index":{"Probabilistically":[0],"quantifying":[1],"uncertainty":[2],"in":[3,68],"parameters,":[4],"predictions":[5],"and":[6,15,55],"decisions":[7],"is":[8,19,49,91],"a":[9,51,74],"crucial":[10],"component":[11],"of":[12,25,62],"broad":[13],"scientific":[14],"engineering":[16],"applications.":[17,70],"This":[18,71,89],"however":[20],"difficult":[21],"if":[22],"the":[23,29,85],"number":[24],"parameters":[26],"far":[27],"exceeds":[28],"sample":[30],"size.":[31],"Although":[32],"there":[33,48],"are":[34],"currently":[35],"many":[36],"methods":[37,80],"which":[38],"have":[39],"guarantees":[40],"for":[41,65,99],"problems":[42],"characterized":[43],"by":[44],"large":[45],"random":[46],"matrices,":[47],"often":[50],"gap":[52],"between":[53],"theory":[54],"practice":[56],"when":[57],"it":[58],"comes":[59],"to":[60,81,84,93],"measures":[61],"statistical":[63],"significance":[64],"matrices":[66],"encountered":[67],"real-world":[69],"paper":[72],"proposes":[73],"scalable":[75],"framework":[76,90],"that":[77],"utilizes":[78],"state-of-the-art":[79],"provide":[82],"approximations":[83],"marginal":[86,95],"posterior":[87,96],"distributions.":[88],"used":[92],"approximate":[94],"inclusion":[97],"probabilities":[98],"Bayesian":[100],"variable":[101],"selection.":[102]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
