{"id":"https://openalex.org/W4283751653","doi":"https://doi.org/10.1080/03610918.2022.2093372","title":"Approximate Bayesian estimator for the random-coefficients model","display_name":"Approximate Bayesian estimator for the random-coefficients model","publication_year":2022,"publication_date":"2022-07-01","ids":{"openalex":"https://openalex.org/W4283751653","doi":"https://doi.org/10.1080/03610918.2022.2093372"},"language":"en","primary_location":{"id":"doi:10.1080/03610918.2022.2093372","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2022.2093372","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/A5041999079","display_name":"Jie Jiang Lichun Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jie Jiang Lichun Wang","raw_affiliation_strings":["Department of Mathematics, Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mathematics, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5041999079"],"corresponding_institution_ids":["https://openalex.org/I21193070"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.07206759,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"53","issue":"6","first_page":"2579","last_page":"2594"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.9988999962806702,"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/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.9988999962806702,"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/T10136","display_name":"Statistical Methods and Inference","score":0.9977999925613403,"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":0.9976000189781189,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/minimum-variance-unbiased-estimator","display_name":"Minimum-variance unbiased estimator","score":0.7946916818618774},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.7399764060974121},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.687197208404541},{"id":"https://openalex.org/keywords/invariant-estimator","display_name":"Invariant estimator","score":0.6831299066543579},{"id":"https://openalex.org/keywords/bias-of-an-estimator","display_name":"Bias of an estimator","score":0.6513485908508301},{"id":"https://openalex.org/keywords/efficient-estimator","display_name":"Efficient estimator","score":0.6258531212806702},{"id":"https://openalex.org/keywords/bayes-estimator","display_name":"Bayes estimator","score":0.6153987050056458},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.6071337461471558},{"id":"https://openalex.org/keywords/minimax-estimator","display_name":"Minimax estimator","score":0.5620555877685547},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.5485051274299622},{"id":"https://openalex.org/keywords/consistent-estimator","display_name":"Consistent estimator","score":0.5092057585716248},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.5040546655654907},{"id":"https://openalex.org/keywords/trimmed-estimator","display_name":"Trimmed estimator","score":0.4514346718788147}],"concepts":[{"id":"https://openalex.org/C165646398","wikidata":"https://www.wikidata.org/wiki/Q3755281","display_name":"Minimum-variance unbiased estimator","level":3,"score":0.7946916818618774},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.7399764060974121},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.687197208404541},{"id":"https://openalex.org/C38689907","wikidata":"https://www.wikidata.org/wiki/Q6059509","display_name":"Invariant estimator","level":5,"score":0.6831299066543579},{"id":"https://openalex.org/C191393472","wikidata":"https://www.wikidata.org/wiki/Q15222032","display_name":"Bias of an estimator","level":4,"score":0.6513485908508301},{"id":"https://openalex.org/C35594927","wikidata":"https://www.wikidata.org/wiki/Q2265984","display_name":"Efficient estimator","level":4,"score":0.6258531212806702},{"id":"https://openalex.org/C68022304","wikidata":"https://www.wikidata.org/wiki/Q842217","display_name":"Bayes estimator","level":3,"score":0.6153987050056458},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.6071337461471558},{"id":"https://openalex.org/C133939421","wikidata":"https://www.wikidata.org/wiki/Q6865379","display_name":"Minimax estimator","level":4,"score":0.5620555877685547},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.5485051274299622},{"id":"https://openalex.org/C164172150","wikidata":"https://www.wikidata.org/wiki/Q1782585","display_name":"Consistent estimator","level":4,"score":0.5092057585716248},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.5040546655654907},{"id":"https://openalex.org/C41020250","wikidata":"https://www.wikidata.org/wiki/Q17144114","display_name":"Trimmed estimator","level":5,"score":0.4514346718788147}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/03610918.2022.2093372","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2022.2093372","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":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W779680562","https://openalex.org/W1906331889","https://openalex.org/W1975650452","https://openalex.org/W1979663020","https://openalex.org/W1996957126","https://openalex.org/W2000084758","https://openalex.org/W2000991338","https://openalex.org/W2017696952","https://openalex.org/W2019747837","https://openalex.org/W2023245001","https://openalex.org/W2049963155","https://openalex.org/W2092520850","https://openalex.org/W2105380431","https://openalex.org/W2148834149","https://openalex.org/W2172171539","https://openalex.org/W2277048178","https://openalex.org/W2403035479","https://openalex.org/W2467183748","https://openalex.org/W2899397672","https://openalex.org/W2904579105","https://openalex.org/W4249735736","https://openalex.org/W4299677235","https://openalex.org/W4388319941","https://openalex.org/W6645339558","https://openalex.org/W6780038297"],"related_works":["https://openalex.org/W2036696807","https://openalex.org/W3175444263","https://openalex.org/W2375720303","https://openalex.org/W2359371365","https://openalex.org/W2731761402","https://openalex.org/W2385938868","https://openalex.org/W4387582659","https://openalex.org/W2462295642","https://openalex.org/W2350206592","https://openalex.org/W2106799280"],"abstract_inverted_index":{"This":[0],"article":[1],"constructs":[2],"an":[3],"approximate":[4,59,96],"Bayesian":[5,60,97],"estimator":[6,31,51,61,78,85,98],"for":[7],"the":[8,27,39,48,54,57,75,81,88,95],"parameter":[9],"vector":[10],"consisted":[11],"of":[12,29,38,87],"variance":[13,30,89],"components":[14],"in":[15,36],"a":[16,100],"random-coefficients":[17],"regression":[18],"(RCR)":[19],"model":[20],"with":[21,47,74],"unbalanced":[22],"data.":[23],"Its":[24],"superiority":[25],"over":[26],"analysis":[28],"(ANOVAE)":[32],"is":[33,62],"strictly":[34],"proved":[35],"terms":[37],"mean":[40],"squared":[41],"error":[42],"matrix":[43],"(MSEM)":[44],"criterion.":[45],"Compared":[46],"usual":[49],"Bayes":[50],"computed":[52],"via":[53],"MCMC":[55],"method,":[56],"proposed":[58],"simple":[63],"and":[64,68,80],"easy":[65],"to":[66],"interpret":[67],"use.":[69],"Also,":[70],"we":[71],"compare":[72],"it":[73],"maximum":[76,83],"likelihood":[77,84],"(MLE)":[79],"restricted":[82],"(RMLE)":[86],"components.":[90],"Numerical":[91],"computations":[92],"show":[93],"that":[94],"has":[99],"good":[101],"approximation":[102],"performance.":[103]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}