{"id":"https://openalex.org/W2527402990","doi":"https://doi.org/10.1080/03610918.2016.1241401","title":"Detecting homogenous predictors in high-dimensional panel model with an MCMC algorithm","display_name":"Detecting homogenous predictors in high-dimensional panel model with an MCMC algorithm","publication_year":2016,"publication_date":"2016-09-30","ids":{"openalex":"https://openalex.org/W2527402990","doi":"https://doi.org/10.1080/03610918.2016.1241401","mag":"2527402990"},"language":"en","primary_location":{"id":"doi:10.1080/03610918.2016.1241401","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2016.1241401","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/A5101863437","display_name":"Ronghua Luo","orcid":"https://orcid.org/0000-0003-0212-4798"},"institutions":[{"id":"https://openalex.org/I204831749","display_name":"Southwestern University of Finance and Economics","ror":"https://ror.org/04ewct822","country_code":"CN","type":"education","lineage":["https://openalex.org/I204831749"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ronghua Luo","raw_affiliation_strings":["School of Finance, Southwestern University of Finance and Economics, Chengdu, Sichuan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Finance, Southwestern University of Finance and Economics, Chengdu, Sichuan, China","institution_ids":["https://openalex.org/I204831749"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111470011","display_name":"Wei Lan","orcid":null},"institutions":[{"id":"https://openalex.org/I204831749","display_name":"Southwestern University of Finance and Economics","ror":"https://ror.org/04ewct822","country_code":"CN","type":"education","lineage":["https://openalex.org/I204831749"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wei Lan","raw_affiliation_strings":["Statistics School, Southwestern University of Finance and Economics, Chengdu, Sichuan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Statistics School, Southwestern University of Finance and Economics, Chengdu, Sichuan, China","institution_ids":["https://openalex.org/I204831749"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5111470011"],"corresponding_institution_ids":["https://openalex.org/I204831749"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.09024858,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"46","issue":"9","first_page":"7376","last_page":"7392"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11911","display_name":"Spatial and Panel Data Analysis","score":0.9948999881744385,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11911","display_name":"Spatial and Panel Data Analysis","score":0.9948999881744385,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10007","display_name":"Monetary Policy and Economic Impact","score":0.9858999848365784,"subfield":{"id":"https://openalex.org/subfields/2000","display_name":"General Economics, Econometrics and Finance"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10591","display_name":"Theoretical and Computational Physics","score":0.98089998960495,"subfield":{"id":"https://openalex.org/subfields/3104","display_name":"Condensed Matter Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.8953797817230225},{"id":"https://openalex.org/keywords/panel-data","display_name":"Panel data","score":0.7128916382789612},{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.7041752338409424},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.5742802619934082},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5676964521408081},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.560947060585022},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5492163896560669},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.37906408309936523},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.32018721103668213},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3064013719558716},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.262484073638916}],"concepts":[{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.8953797817230225},{"id":"https://openalex.org/C6422946","wikidata":"https://www.wikidata.org/wiki/Q857354","display_name":"Panel data","level":2,"score":0.7128916382789612},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.7041752338409424},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.5742802619934082},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5676964521408081},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.560947060585022},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5492163896560669},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.37906408309936523},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.32018721103668213},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3064013719558716},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.262484073638916}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/03610918.2016.1241401","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2016.1241401","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":26,"referenced_works":["https://openalex.org/W905159","https://openalex.org/W630165792","https://openalex.org/W1489808281","https://openalex.org/W1515789335","https://openalex.org/W1541705153","https://openalex.org/W1574610441","https://openalex.org/W1593060747","https://openalex.org/W1659308392","https://openalex.org/W1975650452","https://openalex.org/W1981049247","https://openalex.org/W2048543653","https://openalex.org/W2058815839","https://openalex.org/W2076594196","https://openalex.org/W2097978133","https://openalex.org/W2126330786","https://openalex.org/W2168175751","https://openalex.org/W2491861244","https://openalex.org/W2799006139","https://openalex.org/W2989686067","https://openalex.org/W3098364281","https://openalex.org/W3121418714","https://openalex.org/W3123095200","https://openalex.org/W3124321936","https://openalex.org/W3124354616","https://openalex.org/W3124968907","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W2905433371","https://openalex.org/W4390569940","https://openalex.org/W2888392564","https://openalex.org/W4361193272","https://openalex.org/W4310278675","https://openalex.org/W4388422664","https://openalex.org/W2806259446","https://openalex.org/W2963326959","https://openalex.org/W4312407344","https://openalex.org/W2894289927"],"abstract_inverted_index":{"In":[0,62],"panel":[1,42,70,101],"data":[2],"analysis,":[3],"predictors":[4,13,36,95],"may":[5],"impact":[6],"response":[7],"in":[8,15,25,40,96,99],"substantially":[9],"different":[10],"manner.":[11],"Some":[12],"are":[14,24],"homogenous":[16,60,97],"effects":[17,98],"across":[18],"all":[19],"individuals,":[20],"while":[21],"the":[22,45,94,117],"others":[23],"heterogenous":[26],"way.":[27],"How":[28],"to":[29,56,92],"effectively":[30],"differentiate":[31],"these":[32],"two":[33],"kinds":[34],"of":[35,47,116],"is":[37,120],"crucial,":[38],"particularly":[39],"high-dimensional":[41,100],"data,":[43],"since":[44],"number":[46],"parameters":[48],"should":[49],"be":[50],"greatly":[51],"reduced":[52],"and":[53],"hence":[54],"lead":[55],"better":[57],"interpretability":[58],"by":[59,123],"assumption.":[61],"this":[63,109],"article,":[64],"based":[65],"on":[66],"a":[67,75,87,124],"hierarchical":[68],"Bayesian":[69],"regression":[71],"model,":[72],"we":[73],"propose":[74],"novel":[76],"yet":[77],"effective":[78],"Markov":[79],"chain":[80],"Monte":[81,104],"Carlo":[82,105],"(MCMC)":[83],"algorithm":[84,111],"together":[85],"with":[86],"simple":[88],"maximum":[89],"ratio":[90],"criterion":[91],"detect":[93],"data.":[102],"Extensive":[103],"simulations":[106],"show":[107],"that":[108],"MCMC":[110],"performs":[112],"well.":[113],"The":[114],"usefulness":[115],"proposed":[118],"method":[119],"further":[121],"demonstrated":[122],"real":[125],"example":[126],"from":[127],"China":[128],"financial":[129],"market.":[130]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}