{"id":"https://openalex.org/W3012109881","doi":"https://doi.org/10.1109/cdc40024.2019.9029944","title":"Convergence of Parameter Estimates for Regularized Mixed Linear Regression Models","display_name":"Convergence of Parameter Estimates for Regularized Mixed Linear Regression Models","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W3012109881","doi":"https://doi.org/10.1109/cdc40024.2019.9029944","mag":"3012109881"},"language":"en","primary_location":{"id":"doi:10.1109/cdc40024.2019.9029944","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cdc40024.2019.9029944","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 58th Conference on Decision and Control (CDC)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5079197814","display_name":"Taiyao Wang","orcid":"https://orcid.org/0000-0002-0331-3892"},"institutions":[{"id":"https://openalex.org/I111088046","display_name":"Boston University","ror":"https://ror.org/05qwgg493","country_code":"US","type":"education","lineage":["https://openalex.org/I111088046"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Taiyao Wang","raw_affiliation_strings":["Division of Systems Engineering, Boston University, Boston, MA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Division of Systems Engineering, Boston University, Boston, MA, USA","institution_ids":["https://openalex.org/I111088046"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075696701","display_name":"Ioannis Ch. Paschalidis","orcid":"https://orcid.org/0000-0002-3343-2913"},"institutions":[{"id":"https://openalex.org/I111088046","display_name":"Boston University","ror":"https://ror.org/05qwgg493","country_code":"US","type":"education","lineage":["https://openalex.org/I111088046"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ioannis Ch. Paschalidis","raw_affiliation_strings":["Division of Systems Engineering, Boston University, Boston, MA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Division of Systems Engineering, Boston University, Boston, MA, USA","institution_ids":["https://openalex.org/I111088046"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I111088046"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"19","issue":null,"first_page":"3664","last_page":"3669"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9995999932289124,"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":0.9995999932289124,"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":0.9980999827384949,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9932000041007996,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.6306589245796204},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.6160711050033569},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.5672547817230225},{"id":"https://openalex.org/keywords/linear-programming","display_name":"Linear programming","score":0.5328802466392517},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.49750736355781555},{"id":"https://openalex.org/keywords/martingale","display_name":"Martingale (probability theory)","score":0.4952639639377594},{"id":"https://openalex.org/keywords/counterexample","display_name":"Counterexample","score":0.4932357668876648},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4811469316482544},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.4227648079395294},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3327377736568451},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3312644362449646},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3102905750274658},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.20521357655525208},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.185573011636734}],"concepts":[{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.6306589245796204},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.6160711050033569},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.5672547817230225},{"id":"https://openalex.org/C41045048","wikidata":"https://www.wikidata.org/wiki/Q202843","display_name":"Linear programming","level":2,"score":0.5328802466392517},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.49750736355781555},{"id":"https://openalex.org/C48406656","wikidata":"https://www.wikidata.org/wiki/Q534112","display_name":"Martingale (probability theory)","level":2,"score":0.4952639639377594},{"id":"https://openalex.org/C162838799","wikidata":"https://www.wikidata.org/wiki/Q596077","display_name":"Counterexample","level":2,"score":0.4932357668876648},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4811469316482544},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4227648079395294},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3327377736568451},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3312644362449646},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3102905750274658},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.20521357655525208},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.185573011636734},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cdc40024.2019.9029944","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cdc40024.2019.9029944","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 58th Conference on Decision and Control (CDC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W1505725179","https://openalex.org/W1981121741","https://openalex.org/W2000352206","https://openalex.org/W2006157226","https://openalex.org/W2026693824","https://openalex.org/W2033207252","https://openalex.org/W2036492038","https://openalex.org/W2099020507","https://openalex.org/W2124428910","https://openalex.org/W2129673637","https://openalex.org/W2135046866","https://openalex.org/W2138967244","https://openalex.org/W2164092299","https://openalex.org/W2184753682","https://openalex.org/W2185358546","https://openalex.org/W2199135087","https://openalex.org/W2518479542","https://openalex.org/W2550703763","https://openalex.org/W2570850240","https://openalex.org/W2601154487","https://openalex.org/W2890661317","https://openalex.org/W2893687496","https://openalex.org/W2903277834","https://openalex.org/W2962737134","https://openalex.org/W2963307262","https://openalex.org/W2963351303","https://openalex.org/W2963925379","https://openalex.org/W2963989903","https://openalex.org/W3104105633","https://openalex.org/W4294284923","https://openalex.org/W6638061900","https://openalex.org/W6674896736","https://openalex.org/W6678489871","https://openalex.org/W6680662047","https://openalex.org/W6686540645","https://openalex.org/W6726620310","https://openalex.org/W6729363533","https://openalex.org/W6734989811","https://openalex.org/W6748832166","https://openalex.org/W6753755836","https://openalex.org/W6755506522"],"related_works":["https://openalex.org/W2349952486","https://openalex.org/W1489260687","https://openalex.org/W2775700832","https://openalex.org/W2587222766","https://openalex.org/W3204670144","https://openalex.org/W2163101548","https://openalex.org/W1988934358","https://openalex.org/W4389574038","https://openalex.org/W4293643564","https://openalex.org/W4387718369"],"abstract_inverted_index":{"We":[0,30,48,132,158],"consider":[1],"Mixed":[2,33],"Linear":[3],"Regression":[4],"(MLR),":[5],"where":[6,98],"training":[7,55,90,99,124],"data":[8,100,125],"have":[9],"been":[10],"generated":[11],"from":[12,87,102],"a":[13,32,103,113,134],"mixture":[14],"of":[15,54,71,141],"distinct":[16],"linear":[17],"models":[18],"(or":[19],"clusters)":[20],"and":[21],"we":[22,78,106],"seek":[23],"to":[24,41,63,74,117,147],"identify":[25],"the":[26,45,52,59,64,69,82,85,89,95,109,118,139,143,149,164],"corresponding":[27,110],"coefficient":[28,46,66,120,151],"vectors.":[29,47],"introduce":[31],"Integer":[34],"Programming":[35],"(MIP)":[36],"formulation":[37],"for":[38,153],"MLR":[39],"subject":[40],"regularization":[42],"constraints":[43],"on":[44],"establish":[49,80,107],"that":[50,81,108,115,137],"as":[51],"number":[53],"samples":[56,91],"grows":[57],"large,":[58],"MIP":[60,83,111,144,165],"solution":[61,114],"converges":[62,116],"true":[65,119,150],"vectors":[67,152],"in":[68,138,167],"absence":[70],"noise.":[72,131,171],"Subject":[73],"slightly":[75],"stronger":[76],"assumptions,":[77],"also":[79,159],"identifies":[84],"clusters":[86],"which":[88],"were":[92],"generated.":[93],"In":[94],"special":[96],"case":[97],"come":[101],"single":[104],"cluster,":[105],"yields":[112],"vector":[121],"even":[122],"when":[123],"are":[126],"perturbed":[127],"by":[128],"(martingale":[129],"difference)":[130],"provide":[133,160],"counterexample":[135],"indicating":[136],"presence":[140],"noise,":[142],"may":[145],"fail":[146],"produce":[148],"more":[154],"than":[155],"one":[156],"clusters.":[157],"numerical":[161],"results":[162],"testing":[163],"solutions":[166],"synthetic":[168],"examples":[169],"with":[170]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}