{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/106411"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/106411","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"A Bayesian solution to non-convergence of crossed random effects models","abstract":"When observations (trials) are nested within combinations of subjects and stimuli, crossed random effects models simultaneously take into account both fixed effects and random effects of the subjects and stimuli; however, maximum likelihood estimation (MLE) and restricted maximum likelihood (REML) estimation often encounter convergence problems, which in turn lead to researchers fitting simpler models (e.g., only random intercepts). If the random effect structure is too simple, tests of fixed effects are not valid; if the random effect structure is too complex, tests of fixed effects are inefficient. This study examines issues of estimation, convergence and problems inherent with MLE and REML. We investigated whether Bayesian estimation can solve the convergence problem through a simulation study, which makes a case for adopting a Bayesian approach for estimation, especially when using crossed random effects models. In our simulation study were found that both MLE and REML encountered convergence problems even when trying to fit the correctly specified model and simpler versions of it. Bayesian estimation of models converged 100% of the time, and for the correctly specified model, the parameter estimates were accurate estimates for both fixed and random effects and were essentially unbiased. In sum, the Bayesian approach is a viable alternative to MLE/REML, because models fit by Bayesian estimation solves the non-convergence problem and yields valid and efficient estimates.","abstract_html":"When observations (trials) are nested within combinations of subjects and stimuli, crossed random effects models simultaneously take into account both fixed effects and random effects of the subjects and stimuli; however, maximum likelihood estimation (MLE) and restricted maximum likelihood (REML) estimation often encounter convergence problems, which in turn lead to researchers fitting simpler models (e.g., only random intercepts). If the random effect structure is too simple, tests of fixed effects are not valid; if the random effect structure is too complex, tests of fixed effects are inefficient. This study examines issues of estimation, convergence and problems inherent with MLE and REML. We investigated whether Bayesian estimation can solve the convergence problem through a simulation study, which makes a case for adopting a Bayesian approach for estimation, especially when using crossed random effects models. In our simulation study were found that both MLE and REML encountered convergence problems even when trying to fit the correctly specified model and simpler versions of it. Bayesian estimation of models converged 100% of the time, and for the correctly specified model, the parameter estimates were accurate estimates for both fixed and random effects and were essentially unbiased. In sum, the Bayesian approach is a viable alternative to MLE/REML, because models fit by Bayesian estimation solves the non-convergence problem and yields valid and efficient estimates.","abstract_has_math":false,"creators":["Huang, Mingya"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Educational Psychology","degree_department":null,"school":null,"contributors":["Anderson, Carolyn Jane","Kern, Justin","Zhang, Jinming"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-03-02T22:28:40Z","date_published":"2020-03-02T22:28:40Z","updated_at":"2026-07-22T22:24:47Z","subjects":["Crossed-Random Effects Models, convergence, MLE, REML, Bayesian model estimation"],"languages":["en"],"rights":["Copyright 2019 Mingya Huang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/106411","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Anderson, Carolyn Jane","Kern, Justin","Zhang, Jinming"]},{"key":"dc:creator","label":"Author","values":["Huang, Mingya"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020-03-02T22:28:40Z","2022-03-03T10:15:25Z","2019-07-22","2019-12"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Educational Psychology"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Crossed-Random Effects Models, convergence, MLE, REML, Bayesian model estimation"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2019 Mingya Huang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/106411"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["When observations (trials) are nested within combinations of subjects and stimuli, crossed random effects models simultaneously take into account both fixed effects and random effects of the subjects and stimuli; however, maximum likelihood estimation (MLE) and restricted maximum likelihood (REML) estimation often encounter convergence problems, which in turn lead to researchers fitting simpler models (e.g., only random intercepts). If the random effect structure is too simple, tests of fixed effects are not valid; if the random effect structure is too complex, tests of fixed effects are inefficient. This study examines issues of estimation, convergence and problems inherent with MLE and REML. We investigated whether Bayesian estimation can solve the convergence problem through a simulation study, which makes a case for adopting a Bayesian approach for estimation, especially when using crossed random effects models. In our simulation study were found that both MLE and REML encountered convergence problems even when trying to fit the correctly specified model and simpler versions of it. Bayesian estimation of models converged 100% of the time, and for the correctly specified model, the parameter estimates were accurate estimates for both fixed and random effects and were essentially unbiased. In sum, the Bayesian approach is a viable alternative to MLE/REML, because models fit by Bayesian estimation solves the non-convergence problem and yields valid and efficient estimates.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2021-12-01","The student, Mingya Huang, accepted the attached license on 2019-07-19 at 12:19.","The student, Mingya Huang, submitted this Thesis for approval on 2019-07-19 at 12:50.","This Thesis was approved for publication on 2019-07-22 at 11:20.","DSpace SAF Submission Ingestion Package generated from Vireo submission #14392 on 2020-02-28 at 17:34:55","Made available in DSpace on 2020-03-02T22:28:40Z (GMT). No. of bitstreams: 2 HUANG-THESIS-2019.pdf: 1677376 bytes, checksum: 47f137b43b6c7b7417caac759a4524d2 (MD5) LICENSE.txt: 4209 bytes, checksum: f97222b2bf1d3ee74a3932a147a48021 (MD5) Previous issue date: 2019-07-22","Embargo set by: Seth Robbins for item 113954 Lift date: 2022-03-02T22:28:46Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Embargo set by: Seth Robbins for item 113954 Lift date: 2022-03-02T22:38:05Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Embargo set by: Seth Robbins for item 113954 Lift date: 2022-03-02T22:39:04Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Limited Restriction Lifted for Item 113954 on 2022-03-03T10:15:25Z."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["A Bayesian solution to non-convergence of crossed random effects models"]}]}],"canonical_facts":{"dc:contributor":["Anderson, Carolyn Jane","Kern, Justin","Zhang, Jinming"],"dc:creator":["Huang, Mingya"],"dc:date":["2020-03-02T22:28:40Z","2022-03-03T10:15:25Z","2019-07-22","2019-12"],"dc:description":["When observations (trials) are nested within combinations of subjects and stimuli, crossed random effects models simultaneously take into account both fixed effects and random effects of the subjects and stimuli; however, maximum likelihood estimation (MLE) and restricted maximum likelihood (REML) estimation often encounter convergence problems, which in turn lead to researchers fitting simpler models (e.g., only random intercepts). If the random effect structure is too simple, tests of fixed effects are not valid; if the random effect structure is too complex, tests of fixed effects are inefficient. This study examines issues of estimation, convergence and problems inherent with MLE and REML. We investigated whether Bayesian estimation can solve the convergence problem through a simulation study, which makes a case for adopting a Bayesian approach for estimation, especially when using crossed random effects models. In our simulation study were found that both MLE and REML encountered convergence problems even when trying to fit the correctly specified model and simpler versions of it. Bayesian estimation of models converged 100% of the time, and for the correctly specified model, the parameter estimates were accurate estimates for both fixed and random effects and were essentially unbiased. In sum, the Bayesian approach is a viable alternative to MLE/REML, because models fit by Bayesian estimation solves the non-convergence problem and yields valid and efficient estimates.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2021-12-01","The student, Mingya Huang, accepted the attached license on 2019-07-19 at 12:19.","The student, Mingya Huang, submitted this Thesis for approval on 2019-07-19 at 12:50.","This Thesis was approved for publication on 2019-07-22 at 11:20.","DSpace SAF Submission Ingestion Package generated from Vireo submission #14392 on 2020-02-28 at 17:34:55","Made available in DSpace on 2020-03-02T22:28:40Z (GMT). No. of bitstreams: 2 HUANG-THESIS-2019.pdf: 1677376 bytes, checksum: 47f137b43b6c7b7417caac759a4524d2 (MD5) LICENSE.txt: 4209 bytes, checksum: f97222b2bf1d3ee74a3932a147a48021 (MD5) Previous issue date: 2019-07-22","Embargo set by: Seth Robbins for item 113954 Lift date: 2022-03-02T22:28:46Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Embargo set by: Seth Robbins for item 113954 Lift date: 2022-03-02T22:38:05Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Embargo set by: Seth Robbins for item 113954 Lift date: 2022-03-02T22:39:04Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Limited Restriction Lifted for Item 113954 on 2022-03-03T10:15:25Z."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/106411"],"dc:language":["en"],"dc:rights":["Copyright 2019 Mingya Huang"],"dc:subject":["Crossed-Random Effects Models, convergence, MLE, REML, Bayesian model estimation"],"dc:title":["A Bayesian solution to non-convergence of crossed random effects models"],"dc:type":["text"],"thesis:degree_discipline":["Educational Psychology"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:47Z"}