{"id":"https://openalex.org/W3092208288","doi":"https://doi.org/10.1080/03610918.2020.1827266","title":"Bayesian estimation of the biasing parameter for ridge regression: A novel approach","display_name":"Bayesian estimation of the biasing parameter for ridge regression: A novel approach","publication_year":2020,"publication_date":"2020-10-07","ids":{"openalex":"https://openalex.org/W3092208288","doi":"https://doi.org/10.1080/03610918.2020.1827266","mag":"3092208288"},"language":"en","primary_location":{"id":"doi:10.1080/03610918.2020.1827266","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2020.1827266","pdf_url":null,"source":{"id":"https://openalex.org/S153329750","display_name":"Communications in Statistics - Simulation and Computation","issn_l":"0361-0918","issn":["0361-0918","1532-4141"],"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":"Communications in Statistics - Simulation and Computation","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/A5002918022","display_name":"Fareeha Rashid","orcid":null},"institutions":[{"id":"https://openalex.org/I127670440","display_name":"Bahauddin Zakariya University","ror":"https://ror.org/05x817c41","country_code":"PK","type":"education","lineage":["https://openalex.org/I127670440"]}],"countries":["PK"],"is_corresponding":false,"raw_author_name":"Fareeha Rashid","raw_affiliation_strings":["Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan","institution_ids":["https://openalex.org/I127670440"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082329058","display_name":"Saima Altaf","orcid":"https://orcid.org/0000-0003-0540-981X"},"institutions":[{"id":"https://openalex.org/I127670440","display_name":"Bahauddin Zakariya University","ror":"https://ror.org/05x817c41","country_code":"PK","type":"education","lineage":["https://openalex.org/I127670440"]}],"countries":["PK"],"is_corresponding":true,"raw_author_name":"Saima Altaf","raw_affiliation_strings":["Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan","institution_ids":["https://openalex.org/I127670440"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011827406","display_name":"Muhammad Aslam","orcid":"https://orcid.org/0000-0003-2290-2307"},"institutions":[{"id":"https://openalex.org/I127670440","display_name":"Bahauddin Zakariya University","ror":"https://ror.org/05x817c41","country_code":"PK","type":"education","lineage":["https://openalex.org/I127670440"]}],"countries":["PK"],"is_corresponding":false,"raw_author_name":"Muhammad Aslam","raw_affiliation_strings":["Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan"],"raw_orcid":"https://orcid.org/0000-0003-2290-2307","affiliations":[{"raw_affiliation_string":"Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan","institution_ids":["https://openalex.org/I127670440"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5082329058"],"corresponding_institution_ids":["https://openalex.org/I127670440"],"apc_list":null,"apc_paid":null,"fwci":0.4276,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.66166702,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"51","issue":"12","first_page":"7215","last_page":"7225"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9998999834060669,"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":0.9998999834060669,"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9901000261306763,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11443","display_name":"Advanced Statistical Process Monitoring","score":0.982200026512146,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/multicollinearity","display_name":"Multicollinearity","score":0.9393693804740906},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.7140926718711853},{"id":"https://openalex.org/keywords/ordinary-least-squares","display_name":"Ordinary least squares","score":0.542698323726654},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5335025787353516},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.5235026478767395},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.5223128199577332},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5012941360473633},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.4754197895526886},{"id":"https://openalex.org/keywords/ridge","display_name":"Ridge","score":0.47182947397232056},{"id":"https://openalex.org/keywords/bayesian-linear-regression","display_name":"Bayesian linear regression","score":0.4582078456878662},{"id":"https://openalex.org/keywords/variance-inflation-factor","display_name":"Variance inflation factor","score":0.44121283292770386},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.43743646144866943},{"id":"https://openalex.org/keywords/bayes-estimator","display_name":"Bayes estimator","score":0.42345547676086426},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.40897390246391296},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.3458417057991028},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.3134914040565491}],"concepts":[{"id":"https://openalex.org/C189285262","wikidata":"https://www.wikidata.org/wiki/Q1332350","display_name":"Multicollinearity","level":3,"score":0.9393693804740906},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.7140926718711853},{"id":"https://openalex.org/C99656134","wikidata":"https://www.wikidata.org/wiki/Q2912993","display_name":"Ordinary least squares","level":2,"score":0.542698323726654},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5335025787353516},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.5235026478767395},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.5223128199577332},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5012941360473633},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.4754197895526886},{"id":"https://openalex.org/C32277403","wikidata":"https://www.wikidata.org/wiki/Q740445","display_name":"Ridge","level":2,"score":0.47182947397232056},{"id":"https://openalex.org/C37903108","wikidata":"https://www.wikidata.org/wiki/Q4874474","display_name":"Bayesian linear regression","level":4,"score":0.4582078456878662},{"id":"https://openalex.org/C152732102","wikidata":"https://www.wikidata.org/wiki/Q13434396","display_name":"Variance inflation factor","level":4,"score":0.44121283292770386},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.43743646144866943},{"id":"https://openalex.org/C68022304","wikidata":"https://www.wikidata.org/wiki/Q842217","display_name":"Bayes estimator","level":3,"score":0.42345547676086426},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.40897390246391296},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3458417057991028},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.3134914040565491},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/03610918.2020.1827266","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2020.1827266","pdf_url":null,"source":{"id":"https://openalex.org/S153329750","display_name":"Communications in Statistics - Simulation and Computation","issn_l":"0361-0918","issn":["0361-0918","1532-4141"],"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":"Communications in Statistics - Simulation and Computation","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1579589741","https://openalex.org/W1693427027","https://openalex.org/W1969076842","https://openalex.org/W1969650641","https://openalex.org/W1995139576","https://openalex.org/W1999187230","https://openalex.org/W2015145027","https://openalex.org/W2020364685","https://openalex.org/W2033524410","https://openalex.org/W2046561947","https://openalex.org/W2062991056","https://openalex.org/W2070394662","https://openalex.org/W2071460053","https://openalex.org/W2073334899","https://openalex.org/W2087226271","https://openalex.org/W2093754862","https://openalex.org/W2135497302","https://openalex.org/W2270252036","https://openalex.org/W2568091680","https://openalex.org/W2792189922","https://openalex.org/W2897554753","https://openalex.org/W2899880473","https://openalex.org/W2913252660","https://openalex.org/W4234698323","https://openalex.org/W4249363408"],"related_works":["https://openalex.org/W2586047144","https://openalex.org/W1639044165","https://openalex.org/W3016675171","https://openalex.org/W3021591465","https://openalex.org/W3134815827","https://openalex.org/W2942141314","https://openalex.org/W3186133144","https://openalex.org/W4313294559","https://openalex.org/W4289730765","https://openalex.org/W2154141522"],"abstract_inverted_index":{"Multicollinearity,":[0],"a":[1],"common":[2],"problem":[3,93],"encountered":[4],"in":[5,56,88],"regression":[6,24,48,89,108],"analysis,":[7],"has":[8,101,135],"many":[9,83],"adverse":[10],"effects":[11],"on":[12],"the":[13,20,22,29,43,57,62,66,72,92,105,124,128,140],"ordinary":[14],"least":[15],"squares":[16],"estimator.":[17],"According":[18],"to":[19,32,41,50,71,90,138],"literature,":[21],"ridge":[23,47,107],"estimator":[25,78],"is":[26,39],"one":[27],"of":[28,53,65,82,86,94,98,123],"useful":[30],"remedies":[31],"overcome":[33,91],"this":[34,99],"problem.":[35],"The":[36,96,116],"present":[37],"study":[38],"aimed":[40],"use":[42,51],"Bayesian":[44,58,74],"approach":[45],"for":[46],"and":[49],"estimation":[52],"biasing":[54],"parameters":[55,67],"paradigm":[59],"by":[60,110],"incorporating":[61],"prior":[63],"information":[64],"involved.":[68],"In":[69],"contrary":[70],"available":[73],"estimators,":[75],"our":[76],"proposed":[77,125,141],"permits":[79],"easy":[80],"computation":[81],"posterior":[84],"features":[85],"interest":[87],"multicollinearity.":[95],"performance":[97,122],"technique":[100,126],"been":[102,136],"compared":[103],"with":[104],"well-known":[106],"estimators":[109],"executing":[111],"an":[112,120],"extensive":[113],"simulation":[114],"study.":[115],"numerical":[117],"results":[118],"provided":[119],"exceptional":[121],"using":[127],"mean":[129],"squared":[130],"error":[131],"criterion.":[132],"An":[133],"example":[134],"used":[137],"illustrate":[139],"technique.":[142]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}