{"id":"https://openalex.org/W3123939610","doi":"https://doi.org/10.1080/10618600.2021.1874963","title":"Estimating Multiple Precision Matrices With Cluster Fusion Regularization","display_name":"Estimating Multiple Precision Matrices With Cluster Fusion Regularization","publication_year":2021,"publication_date":"2021-01-19","ids":{"openalex":"https://openalex.org/W3123939610","doi":"https://doi.org/10.1080/10618600.2021.1874963","mag":"3123939610"},"language":"en","primary_location":{"id":"doi:10.1080/10618600.2021.1874963","is_oa":false,"landing_page_url":"https://doi.org/10.1080/10618600.2021.1874963","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":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5068758572","display_name":"Bradley S. Price","orcid":"https://orcid.org/0000-0002-0619-3347"},"institutions":[{"id":"https://openalex.org/I12097938","display_name":"West Virginia University","ror":"https://ror.org/011vxgd24","country_code":"US","type":"education","lineage":["https://openalex.org/I12097938"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Bradley S. Price","raw_affiliation_strings":["Management Information Systems Department, West Virginia University, Morgantown, WV;"],"affiliations":[{"raw_affiliation_string":"Management Information Systems Department, West Virginia University, Morgantown, WV;","institution_ids":["https://openalex.org/I12097938"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016886493","display_name":"Aaron J. Molstad","orcid":"https://orcid.org/0000-0003-0645-5105"},"institutions":[{"id":"https://openalex.org/I33213144","display_name":"University of Florida","ror":"https://ror.org/02y3ad647","country_code":"US","type":"education","lineage":["https://openalex.org/I33213144"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Aaron J. Molstad","raw_affiliation_strings":["Department of Statistics and Genetics Institute, University of Florida, Gainesville, FL;"],"affiliations":[{"raw_affiliation_string":"Department of Statistics and Genetics Institute, University of Florida, Gainesville, FL;","institution_ids":["https://openalex.org/I33213144"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5029287995","display_name":"Ben Sherwood","orcid":"https://orcid.org/0000-0002-4419-8288"},"institutions":[{"id":"https://openalex.org/I146416000","display_name":"University of Kansas","ror":"https://ror.org/001tmjg57","country_code":"US","type":"education","lineage":["https://openalex.org/I146416000"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ben Sherwood","raw_affiliation_strings":["School of Business, University of Kansas, Lawrence, KS"],"affiliations":[{"raw_affiliation_string":"School of Business, University of Kansas, Lawrence, KS","institution_ids":["https://openalex.org/I146416000"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5068758572"],"corresponding_institution_ids":["https://openalex.org/I12097938"],"apc_list":null,"apc_paid":null,"fwci":1.0507,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.71281079,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"30","issue":"4","first_page":"823","last_page":"834"},"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/T10136","display_name":"Statistical Methods and Inference","score":0.9975000023841858,"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":0.9948999881744385,"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/coordinate-descent","display_name":"Coordinate descent","score":0.6092487573623657},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.6029482483863831},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5349401831626892},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.52137690782547},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.4954332113265991},{"id":"https://openalex.org/keywords/convex-optimization","display_name":"Convex optimization","score":0.47035956382751465},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.4562872052192688},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.44817766547203064},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.43623828887939453},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3935275077819824},{"id":"https://openalex.org/keywords/regular-polygon","display_name":"Regular polygon","score":0.29409003257751465},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.17954403162002563}],"concepts":[{"id":"https://openalex.org/C157553263","wikidata":"https://www.wikidata.org/wiki/Q5168004","display_name":"Coordinate descent","level":2,"score":0.6092487573623657},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6029482483863831},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5349401831626892},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.52137690782547},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.4954332113265991},{"id":"https://openalex.org/C157972887","wikidata":"https://www.wikidata.org/wiki/Q463359","display_name":"Convex optimization","level":3,"score":0.47035956382751465},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.4562872052192688},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.44817766547203064},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.43623828887939453},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3935275077819824},{"id":"https://openalex.org/C112680207","wikidata":"https://www.wikidata.org/wiki/Q714886","display_name":"Regular polygon","level":2,"score":0.29409003257751465},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.17954403162002563},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/10618600.2021.1874963","is_oa":false,"landing_page_url":"https://doi.org/10.1080/10618600.2021.1874963","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"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":56,"referenced_works":["https://openalex.org/W149914259","https://openalex.org/W216325278","https://openalex.org/W1480376833","https://openalex.org/W1726806267","https://openalex.org/W1892331524","https://openalex.org/W1969752902","https://openalex.org/W1972239934","https://openalex.org/W1974105260","https://openalex.org/W1977556410","https://openalex.org/W1989493069","https://openalex.org/W1989946722","https://openalex.org/W1998111788","https://openalex.org/W2000185636","https://openalex.org/W2011832962","https://openalex.org/W2019539159","https://openalex.org/W2044600950","https://openalex.org/W2046649434","https://openalex.org/W2052386156","https://openalex.org/W2052793367","https://openalex.org/W2074360197","https://openalex.org/W2081746825","https://openalex.org/W2082503527","https://openalex.org/W2096801452","https://openalex.org/W2100270478","https://openalex.org/W2100556411","https://openalex.org/W2103868202","https://openalex.org/W2114285038","https://openalex.org/W2123470622","https://openalex.org/W2132555912","https://openalex.org/W2144664383","https://openalex.org/W2152904625","https://openalex.org/W2163707651","https://openalex.org/W2165009258","https://openalex.org/W2170563643","https://openalex.org/W2265919274","https://openalex.org/W2346523478","https://openalex.org/W2528307994","https://openalex.org/W2558869262","https://openalex.org/W2559902409","https://openalex.org/W2604272474","https://openalex.org/W2621305858","https://openalex.org/W2949023912","https://openalex.org/W2950266077","https://openalex.org/W2962943048","https://openalex.org/W2963364599","https://openalex.org/W2963489521","https://openalex.org/W2990138404","https://openalex.org/W3013631797","https://openalex.org/W3101035442","https://openalex.org/W3104507223","https://openalex.org/W3105178084","https://openalex.org/W3106319742","https://openalex.org/W6607311024","https://openalex.org/W6608023180","https://openalex.org/W6644682428","https://openalex.org/W6750028727"],"related_works":["https://openalex.org/W1988224349","https://openalex.org/W50353483","https://openalex.org/W2125171642","https://openalex.org/W1504175118","https://openalex.org/W1975700603","https://openalex.org/W2001413153","https://openalex.org/W2953763369","https://openalex.org/W1988190368","https://openalex.org/W2089251650","https://openalex.org/W4295883326"],"abstract_inverted_index":{"We":[0,122],"propose":[1],"a":[2,33,66,83,93,117,128],"penalized":[3,108],"likelihood":[4],"framework":[5,36],"for":[6,42,99,171],"estimating":[7],"multiple":[8],"precision":[9,25,47,53,109,154],"matrices":[10,26,48,155],"from":[11],"different":[12],"classes.":[13],"Most":[14],"existing":[15],"methods":[16,160],"either":[17],"incorporate":[18],"no":[19],"information":[20,30,164],"on":[21],"relationships":[22,50,152],"between":[23,51,82,153],"the":[24,46,52,100],"or":[27],"require":[28,64,103],"this":[29,39,125,172],"be":[31],"known":[32],"priori.":[34],"The":[35],"proposed":[37,73],"in":[38,167],"article":[40,173],"allows":[41],"simultaneous":[43],"estimation":[44,111],"of":[45,62,131],"and":[49,56,92,136,156],"matrices.":[54],"Sparse":[55],"nonsparse":[57],"estimators":[58],"are":[59,174],"proposed,":[60],"both":[61],"which":[63,80,113,161],"solving":[65],"nonconvex":[67],"optimization":[68,85],"problem.":[69,96],"To":[70],"compute":[71],"our":[72,144],"estimators,":[74],"we":[75,114,141],"use":[76,162],"an":[77,105],"iterative":[78],"algorithm":[79],"alternates":[81],"convex":[84],"problem":[86],"solved":[87],"by":[88],"blockwise":[89],"coordinate":[90],"descent":[91,120],"k-means":[94],"clustering":[95],"Blockwise":[97],"updates":[98],"sparse":[101],"estimator":[102],"computing":[104],"elastic":[106],"net":[107],"matrix":[110],"problem,":[112],"solve":[115],"using":[116],"proximal":[118],"gradient":[119],"algorithm.":[121],"prove":[123],"that":[124,143,149],"subalgorithm":[126],"has":[127],"linear":[129],"rate":[130],"convergence.":[132],"In":[133],"simulation":[134],"studies":[135],"two":[137],"real":[138],"data":[139],"applications,":[140],"show":[142],"method":[145],"can":[146],"outperform":[147],"competitors":[148],"ignore":[150],"relevant":[151],"performs":[157],"similarly":[158],"to":[159],"prior":[163],"often":[165],"unknown":[166],"practice.":[168],"Supplementary":[169],"materials":[170],"available":[175],"online.":[176]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}