{"id":"https://openalex.org/W2513574565","doi":"https://doi.org/10.1109/prni.2016.7552328","title":"Regularization parameter selection for a bayesian group sparse multi-task regression model with application to imaging genomics","display_name":"Regularization parameter selection for a bayesian group sparse multi-task regression model with application to imaging genomics","publication_year":2016,"publication_date":"2016-06-01","ids":{"openalex":"https://openalex.org/W2513574565","doi":"https://doi.org/10.1109/prni.2016.7552328","mag":"2513574565"},"language":"en","primary_location":{"id":"doi:10.1109/prni.2016.7552328","is_oa":false,"landing_page_url":"https://doi.org/10.1109/prni.2016.7552328","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Workshop on Pattern Recognition in Neuroimaging (PRNI)","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/A5044169300","display_name":"Farouk S. Nathoo","orcid":"https://orcid.org/0000-0002-2569-3507"},"institutions":[{"id":"https://openalex.org/I212119943","display_name":"University of Victoria","ror":"https://ror.org/04s5mat29","country_code":"CA","type":"education","lineage":["https://openalex.org/I212119943"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Farouk S. Nathoo","raw_affiliation_strings":["Department of Mathematics and Statistics, University of Victoria, Victoria, Canada"],"affiliations":[{"raw_affiliation_string":"Department of Mathematics and Statistics, University of Victoria, Victoria, Canada","institution_ids":["https://openalex.org/I212119943"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033306029","display_name":"Keelin Greenlaw","orcid":null},"institutions":[{"id":"https://openalex.org/I212119943","display_name":"University of Victoria","ror":"https://ror.org/04s5mat29","country_code":"CA","type":"education","lineage":["https://openalex.org/I212119943"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Keelin Greenlaw","raw_affiliation_strings":["Department of Mathematics and Statistics, University of Victoria, Victoria, Canada"],"affiliations":[{"raw_affiliation_string":"Department of Mathematics and Statistics, University of Victoria, Victoria, Canada","institution_ids":["https://openalex.org/I212119943"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085426335","display_name":"Mary Lesperance","orcid":"https://orcid.org/0000-0001-8613-1068"},"institutions":[{"id":"https://openalex.org/I212119943","display_name":"University of Victoria","ror":"https://ror.org/04s5mat29","country_code":"CA","type":"education","lineage":["https://openalex.org/I212119943"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Mary Lesperance","raw_affiliation_strings":["Department of Mathematics and Statistics, University of Victoria, Victoria, Canada"],"affiliations":[{"raw_affiliation_string":"Department of Mathematics and Statistics, University of Victoria, Victoria, Canada","institution_ids":["https://openalex.org/I212119943"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5044169300"],"corresponding_institution_ids":["https://openalex.org/I212119943"],"apc_list":null,"apc_paid":null,"fwci":2.129,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.88390463,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"11","issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9994000196456909,"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":0.9994000196456909,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9922999739646912,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.9886000156402588,"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/hyperparameter","display_name":"Hyperparameter","score":0.6887301802635193},{"id":"https://openalex.org/keywords/marginal-likelihood","display_name":"Marginal likelihood","score":0.6316066980361938},{"id":"https://openalex.org/keywords/lasso","display_name":"Lasso (programming language)","score":0.5500312447547913},{"id":"https://openalex.org/keywords/imaging-genetics","display_name":"Imaging genetics","score":0.5484784245491028},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.511978030204773},{"id":"https://openalex.org/keywords/gibbs-sampling","display_name":"Gibbs sampling","score":0.5097901225090027},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.5095375180244446},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5017063617706299},{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.4646531939506531},{"id":"https://openalex.org/keywords/bayes-factor","display_name":"Bayes factor","score":0.4557582437992096},{"id":"https://openalex.org/keywords/model-selection","display_name":"Model selection","score":0.4434365928173065},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.42495933175086975},{"id":"https://openalex.org/keywords/bayesian-hierarchical-modeling","display_name":"Bayesian hierarchical modeling","score":0.41572141647338867},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36406010389328003},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3518820106983185},{"id":"https://openalex.org/keywords/neuroimaging","display_name":"Neuroimaging","score":0.25945568084716797},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.09471914172172546}],"concepts":[{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.6887301802635193},{"id":"https://openalex.org/C95923904","wikidata":"https://www.wikidata.org/wiki/Q6760420","display_name":"Marginal likelihood","level":3,"score":0.6316066980361938},{"id":"https://openalex.org/C37616216","wikidata":"https://www.wikidata.org/wiki/Q3218363","display_name":"Lasso (programming language)","level":2,"score":0.5500312447547913},{"id":"https://openalex.org/C18183760","wikidata":"https://www.wikidata.org/wiki/Q6002833","display_name":"Imaging genetics","level":3,"score":0.5484784245491028},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.511978030204773},{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.5097901225090027},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.5095375180244446},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5017063617706299},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.4646531939506531},{"id":"https://openalex.org/C142291917","wikidata":"https://www.wikidata.org/wiki/Q4165283","display_name":"Bayes factor","level":4,"score":0.4557582437992096},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.4434365928173065},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42495933175086975},{"id":"https://openalex.org/C191413810","wikidata":"https://www.wikidata.org/wiki/Q17100952","display_name":"Bayesian hierarchical modeling","level":4,"score":0.41572141647338867},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36406010389328003},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3518820106983185},{"id":"https://openalex.org/C58693492","wikidata":"https://www.wikidata.org/wiki/Q551875","display_name":"Neuroimaging","level":2,"score":0.25945568084716797},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.09471914172172546},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.0},{"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":1,"locations":[{"id":"doi:10.1109/prni.2016.7552328","is_oa":false,"landing_page_url":"https://doi.org/10.1109/prni.2016.7552328","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Workshop on Pattern Recognition in Neuroimaging (PRNI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.7699999809265137,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320320994","display_name":"Canada Research Chairs","ror":"https://ror.org/0517h6h17"},{"id":"https://openalex.org/F4320334593","display_name":"Natural Sciences and Engineering Research Council of Canada","ror":"https://ror.org/01h531d29"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W1976365540","https://openalex.org/W1982652137","https://openalex.org/W2123222757","https://openalex.org/W2154865814","https://openalex.org/W6678521640"],"related_works":["https://openalex.org/W2801810851","https://openalex.org/W3208602028","https://openalex.org/W4206258942","https://openalex.org/W3006592192","https://openalex.org/W4391296619","https://openalex.org/W2118048521","https://openalex.org/W3214407380","https://openalex.org/W2004514718","https://openalex.org/W568881065","https://openalex.org/W1994749633"],"abstract_inverted_index":{"We":[0,158,182],"investigate":[1],"the":[2,16,55,61,71,83,95,117,127,143,148,153,170,177,190],"choice":[3],"of":[4,19,33,97,142,179],"tuning":[5,75,98],"parameters":[6,99],"for":[7,15],"a":[8,74,80,121],"Bayesian":[9,85,91],"multi-level":[10],"group":[11],"lasso":[12],"model":[13,26,129],"developed":[14],"joint":[17],"analysis":[18],"neuroimaging":[20],"and":[21,38,48,60,107,165],"genetic":[22,154],"data.":[23],"The":[24],"regression":[25,144],"we":[27,93,132],"consider":[28,94],"relates":[29],"multivariate":[30],"phenotypes":[31],"consisting":[32],"brain":[34],"summary":[35],"measures":[36],"(volumetric":[37],"cortical":[39],"thickness":[40],"values)":[41],"to":[42,58,64,79,139,169,199],"single":[43],"nucleotide":[44],"polymorphism":[45],"(SNPs)":[46],"data":[47],"imposes":[49],"penalization":[50],"at":[51],"two":[52],"nested":[53],"levels,":[54],"first":[56],"corresponding":[57,63],"genes":[59],"second":[62],"SNPs.":[65],"Associated":[66],"with":[67],"each":[68],"level":[69],"in":[70,82,147],"penalty":[72],"is":[73],"parameter":[76,145],"which":[77,173],"corresponds":[78],"hyperparameter":[81],"hierarchical":[84,102],"formulation.":[86],"Following":[87],"previous":[88],"work":[89],"on":[90,105,115,176,189],"lassos":[92],"estimation":[96],"through":[100,111,162],"either":[101],"Bayes":[103,113],"based":[104,114,188],"hyperpriors":[106],"Gibbs":[108],"sampling":[109],"or":[110,151],"empirical":[112],"maximizing":[116],"marginal":[118,171],"likelihood":[119,172],"using":[120],"Monte":[122],"Carlo":[123],"EM":[124],"algorithm.":[125],"For":[126],"specific":[128],"under":[130],"consideration":[131],"find":[133],"that":[134,202],"these":[135,160],"approaches":[136],"can":[137,203],"lead":[138],"severe":[140],"overshrinkage":[141],"estimates":[146],"highdimensional":[149],"setting":[150],"when":[152],"effects":[155],"are":[156],"weak.":[157],"demonstrate":[159],"problems":[161],"simulation":[163],"examples":[164],"study":[166],"an":[167,185,196,208],"approximation":[168,198],"sheds":[174],"light":[175],"cause":[178],"this":[180],"problem.":[181],"then":[183],"suggest":[184],"alternative":[186],"approach":[187],"widely":[191],"applicable":[192],"information":[193],"criterion":[194],"(WAIC),":[195],"asymptotic":[197],"leave-one-out":[200],"crossvalidation":[201],"be":[204],"computed":[205],"conveniently":[206],"within":[207],"MCMC":[209],"framework.":[210]},"counts_by_year":[{"year":2019,"cited_by_count":3},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
