{"id":"https://openalex.org/W3209366373","doi":"https://doi.org/10.1080/03610918.2021.2022697","title":"Mixed models and shrinkage estimation for balanced and unbalanced designs","display_name":"Mixed models and shrinkage estimation for balanced and unbalanced designs","publication_year":2022,"publication_date":"2022-01-03","ids":{"openalex":"https://openalex.org/W3209366373","doi":"https://doi.org/10.1080/03610918.2021.2022697","mag":"3209366373"},"language":"en","primary_location":{"id":"doi:10.1080/03610918.2021.2022697","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2021.2022697","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":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2111.02829","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5009036230","display_name":"Yihan Bao","orcid":"https://orcid.org/0000-0001-6344-0052"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yihan Bao","raw_affiliation_strings":["Department of Statistics and Data Science, Cornell University, Ithaca, New York, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics and Data Science, Cornell University, Ithaca, New York, USA","institution_ids":["https://openalex.org/I205783295"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102954954","display_name":"James G. Booth","orcid":"https://orcid.org/0000-0001-9572-6004"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"James G. Booth","raw_affiliation_strings":["Department of Statistics and Data Science, Cornell University, Ithaca, New York, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics and Data Science, Cornell University, Ithaca, New York, USA","institution_ids":["https://openalex.org/I205783295"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5102954954"],"corresponding_institution_ids":["https://openalex.org/I205783295"],"apc_list":null,"apc_paid":null,"fwci":0.2691,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.70141893,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"53","issue":"1","first_page":"398","last_page":"408"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11674","display_name":"Sports Analytics and Performance","score":0.9980999827384949,"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/T11674","display_name":"Sports Analytics and Performance","score":0.9980999827384949,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/shrinkage","display_name":"Shrinkage","score":0.8337299227714539},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.6906817555427551},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.6813320517539978},{"id":"https://openalex.org/keywords/mixed-model","display_name":"Mixed model","score":0.6574576497077942},{"id":"https://openalex.org/keywords/shrinkage-estimator","display_name":"Shrinkage estimator","score":0.6213627457618713},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.6047347784042358},{"id":"https://openalex.org/keywords/bayes-estimator","display_name":"Bayes estimator","score":0.5857406854629517},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5282843708992004},{"id":"https://openalex.org/keywords/linear-model","display_name":"Linear model","score":0.4999406337738037},{"id":"https://openalex.org/keywords/league","display_name":"League","score":0.495218425989151},{"id":"https://openalex.org/keywords/generalized-linear-mixed-model","display_name":"Generalized linear mixed model","score":0.48559707403182983},{"id":"https://openalex.org/keywords/outcome","display_name":"Outcome (game theory)","score":0.4542056918144226},{"id":"https://openalex.org/keywords/empirical-modelling","display_name":"Empirical modelling","score":0.44048431515693665},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.43180716037750244},{"id":"https://openalex.org/keywords/small-area-estimation","display_name":"Small area estimation","score":0.4293200373649597},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.4178640842437744},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.35353153944015503},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.23905989527702332},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.15796136856079102},{"id":"https://openalex.org/keywords/simulation","display_name":"Simulation","score":0.1316111981868744},{"id":"https://openalex.org/keywords/mathematical-economics","display_name":"Mathematical economics","score":0.08977198600769043}],"concepts":[{"id":"https://openalex.org/C180145272","wikidata":"https://www.wikidata.org/wiki/Q7504144","display_name":"Shrinkage","level":2,"score":0.8337299227714539},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.6906817555427551},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.6813320517539978},{"id":"https://openalex.org/C16012445","wikidata":"https://www.wikidata.org/wiki/Q1501135","display_name":"Mixed model","level":2,"score":0.6574576497077942},{"id":"https://openalex.org/C102592046","wikidata":"https://www.wikidata.org/wiki/Q7504144","display_name":"Shrinkage estimator","level":5,"score":0.6213627457618713},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.6047347784042358},{"id":"https://openalex.org/C68022304","wikidata":"https://www.wikidata.org/wiki/Q842217","display_name":"Bayes estimator","level":3,"score":0.5857406854629517},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5282843708992004},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.4999406337738037},{"id":"https://openalex.org/C207456731","wikidata":"https://www.wikidata.org/wiki/Q660818","display_name":"League","level":2,"score":0.495218425989151},{"id":"https://openalex.org/C153720581","wikidata":"https://www.wikidata.org/wiki/Q5532490","display_name":"Generalized linear mixed model","level":2,"score":0.48559707403182983},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.4542056918144226},{"id":"https://openalex.org/C133199616","wikidata":"https://www.wikidata.org/wiki/Q25386885","display_name":"Empirical modelling","level":2,"score":0.44048431515693665},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.43180716037750244},{"id":"https://openalex.org/C129963666","wikidata":"https://www.wikidata.org/wiki/Q17105857","display_name":"Small area estimation","level":3,"score":0.4293200373649597},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4178640842437744},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.35353153944015503},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.23905989527702332},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.15796136856079102},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.1316111981868744},{"id":"https://openalex.org/C144237770","wikidata":"https://www.wikidata.org/wiki/Q747534","display_name":"Mathematical economics","level":1,"score":0.08977198600769043},{"id":"https://openalex.org/C165646398","wikidata":"https://www.wikidata.org/wiki/Q3755281","display_name":"Minimum-variance unbiased estimator","level":3,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C191393472","wikidata":"https://www.wikidata.org/wiki/Q15222032","display_name":"Bias of an estimator","level":4,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1080/03610918.2021.2022697","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2021.2022697","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"},{"id":"pmh:oai:arXiv.org:2111.02829","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2111.02829","pdf_url":"https://arxiv.org/pdf/2111.02829","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2111.02829","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2111.02829","pdf_url":"https://arxiv.org/pdf/2111.02829","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1584444527","https://openalex.org/W1598266570","https://openalex.org/W1970195793","https://openalex.org/W1991769471","https://openalex.org/W2072857774","https://openalex.org/W2077788215","https://openalex.org/W2122759946","https://openalex.org/W2413573154","https://openalex.org/W2582743722","https://openalex.org/W4302561155","https://openalex.org/W6634927326","https://openalex.org/W7029979171"],"related_works":["https://openalex.org/W2021312236","https://openalex.org/W2186257539","https://openalex.org/W2895736713","https://openalex.org/W2477652392","https://openalex.org/W1551599973","https://openalex.org/W1537371881","https://openalex.org/W3150858532","https://openalex.org/W2549682349","https://openalex.org/W1538644651","https://openalex.org/W3141596217"],"abstract_inverted_index":{"The":[0],"known":[1],"connection":[2],"between":[3],"shrinkage":[4],"estimation,":[5],"empirical":[6,57],"Bayes,":[7],"and":[8,14,18,50],"mixed":[9,31,65],"effects":[10],"models":[11,67],"is":[12,33],"explored":[13],"applied":[15],"to":[16],"balanced":[17],"unbalanced":[19],"designs":[20],"in":[21],"which":[22],"the":[23,37,79],"responses":[24],"are":[25,68],"correlated.":[26],"As":[27],"an":[28],"illustration,":[29],"a":[30],"model":[32],"proposed":[34],"for":[35],"predicting":[36],"outcome":[38],"of":[39],"English":[40],"Premier":[41],"League":[42],"games":[43],"that":[44,74],"takes":[45],"into":[46],"account":[47],"both":[48],"home":[49],"away":[51],"team":[52],"effects.":[53],"Results":[54],"based":[55],"on":[56],"best":[58],"linear":[59,66],"unbiased":[60],"predictors":[61,73],"obtained":[62],"from":[63,78],"fitting":[64],"compared":[69],"with":[70],"fully":[71],"Bayesian":[72],"utilize":[75],"prior":[76],"information":[77],"previous":[80],"season.":[81]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2021-11-08T00:00:00"}
