{"id":"https://openalex.org/W1985242481","doi":"https://doi.org/10.1109/tsp.2011.2177833","title":"The Consistency of MDL for Linear Regression Models With Increasing Signal-to-Noise Ratio","display_name":"The Consistency of MDL for Linear Regression Models With Increasing Signal-to-Noise Ratio","publication_year":2011,"publication_date":"2011-12-06","ids":{"openalex":"https://openalex.org/W1985242481","doi":"https://doi.org/10.1109/tsp.2011.2177833","mag":"1985242481"},"language":"en","primary_location":{"id":"doi:10.1109/tsp.2011.2177833","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2011.2177833","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Signal Processing","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/A5101718298","display_name":"Daniel Schmidt","orcid":"https://orcid.org/0000-0002-5827-6478"},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"The University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Daniel F. Schmidt","raw_affiliation_strings":["Centre for MEGA Epidemiology, University of Melbourne, Carlton, VIC, Australia","Centre for MEGA Epidemiology, University of Melbourne, Carlton, VIC, Australia#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Centre for MEGA Epidemiology, University of Melbourne, Carlton, VIC, Australia","institution_ids":["https://openalex.org/I165779595"]},{"raw_affiliation_string":"Centre for MEGA Epidemiology, University of Melbourne, Carlton, VIC, Australia#TAB#","institution_ids":["https://openalex.org/I165779595"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023193568","display_name":"Enes Makalic","orcid":"https://orcid.org/0000-0003-3017-0871"},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"The University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Enes Makalic","raw_affiliation_strings":["Centre for MEGA Epidemiology, University of Melbourne, Carlton, VIC, Australia","Centre for MEGA Epidemiology, University of Melbourne, Carlton, VIC, Australia#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Centre for MEGA Epidemiology, University of Melbourne, Carlton, VIC, Australia","institution_ids":["https://openalex.org/I165779595"]},{"raw_affiliation_string":"Centre for MEGA Epidemiology, University of Melbourne, Carlton, VIC, Australia#TAB#","institution_ids":["https://openalex.org/I165779595"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I165779595"],"apc_list":null,"apc_paid":null,"fwci":1.3688,"has_fulltext":false,"cited_by_count":32,"citation_normalized_percentile":{"value":0.81578405,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"60","issue":"3","first_page":"1508","last_page":"1510"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11871","display_name":"Advanced Statistical Methods and Models","score":0.995199978351593,"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/T11871","display_name":"Advanced Statistical Methods and Models","score":0.995199978351593,"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":0.9937999844551086,"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/T10136","display_name":"Statistical Methods and 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/minimum-description-length","display_name":"Minimum description length","score":0.8538264036178589},{"id":"https://openalex.org/keywords/bayesian-information-criterion","display_name":"Bayesian information criterion","score":0.7227321267127991},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.6908750534057617},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.6184946894645691},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.5781148672103882},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.5066822171211243},{"id":"https://openalex.org/keywords/model-selection","display_name":"Model selection","score":0.46027350425720215},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.44854894280433655},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.42229175567626953},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3792252540588379},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.343823105096817},{"id":"https://openalex.org/keywords/discrete-mathematics","display_name":"Discrete mathematics","score":0.09338542819023132}],"concepts":[{"id":"https://openalex.org/C87465248","wikidata":"https://www.wikidata.org/wiki/Q1417790","display_name":"Minimum description length","level":2,"score":0.8538264036178589},{"id":"https://openalex.org/C168136583","wikidata":"https://www.wikidata.org/wiki/Q1988242","display_name":"Bayesian information criterion","level":2,"score":0.7227321267127991},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.6908750534057617},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.6184946894645691},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.5781148672103882},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.5066822171211243},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.46027350425720215},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.44854894280433655},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.42229175567626953},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3792252540588379},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.343823105096817},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.09338542819023132}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tsp.2011.2177833","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2011.2177833","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Signal Processing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W1520735736","https://openalex.org/W1973217014","https://openalex.org/W2034230784","https://openalex.org/W2054658115","https://openalex.org/W2060161465","https://openalex.org/W2101460669","https://openalex.org/W2122951883","https://openalex.org/W2142635246","https://openalex.org/W2168175751","https://openalex.org/W2296087697","https://openalex.org/W3123545922"],"related_works":["https://openalex.org/W1582765910","https://openalex.org/W1551708527","https://openalex.org/W2525134339","https://openalex.org/W2544537192","https://openalex.org/W2148249824","https://openalex.org/W2046229974","https://openalex.org/W236172754","https://openalex.org/W2033907215","https://openalex.org/W3202195698","https://openalex.org/W2111808431"],"abstract_inverted_index":{"Recent":[0],"work":[1],"by":[2,56],"Ding":[3,32],"and":[4,33,77,91],"Kay":[5,34],"has":[6,74],"demonstrated":[7],"that":[8,38,64,79],"the":[9,26,39,66,80,96],"Bayesian":[10,92],"information":[11,122],"criterion":[12,69],"(BIC)":[13],"is":[14],"an":[15],"inconsistent":[16,48],"estimator":[17],"of":[18,50,98],"model":[19,23,51],"order":[20,52],"in":[21,53],"nested":[22],"selection":[24],"as":[25,100,109],"noise":[27],"variance":[28],"\u03c4*\u2192":[29,101],"0.":[30,102],"Unfortunately,":[31],"have":[35],"erroneously":[36],"concluded":[37],"minimum":[40],"description":[41],"length":[42],"(MDL)":[43],"principle":[44],"also":[45],"leads":[46],"to":[47,112],"estimates":[49],"this":[54,75],"setting":[55],"equating":[57],"BIC":[58],"with":[59],"MDL.":[60],"This":[61],"correspondence":[62],"shows":[63],"only":[65],"earlier":[67],"MDL":[68,82],"based":[70,86],"on":[71,87],"asymptotic":[72],"assumptions":[73],"problem,":[76],"proves":[78],"new":[81],"linear":[83],"regression":[84,124],"criteria":[85],"normalized":[88],"maximum":[89],"likelihood":[90],"mixture":[93],"codes":[94],"satisfy":[95],"notion":[97],"consistency":[99,116],"The":[103],"main":[104],"result":[105],"may":[106],"be":[107],"used":[108],"a":[110],"basis":[111],"easily":[113],"establish":[114],"similar":[115],"results":[117],"for":[118],"other":[119],"closely":[120],"related":[121],"theoretic":[123],"criteria.":[125]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":6},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":1},{"year":2012,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}