{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/97545"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/97545","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Bayesian Expectation-Maximization-Maximization: a latent-mixture-modeling-based Bayesian algorithm for the three-parameter logistic model","abstract":"The current study proposes a Bayesian Expectation-Maximization-Maximization (Bayesian EMM, or BEMM), which is an alternative feasible Bayesian algorithm for the three-parameter logistic model (3PLM). The Bayesian EMM takes full advantage of both the EMM and the Bayesian approach. The BEMM not only successfully solves the issue of inaccurate estimates for few items in the EMM algorithm, but also alleviates the negative effect caused by different priors in the traditional Bayesian EM. The simulation studies and real data examples indicate that: (1) The Bayesian EMM can produce more accurate and stable item estimates. (2) Standard errors (SE) yielded by the Bayesian EMM tend to be smaller than the traditional Bayesian EM. (3) The Bayesian EMM is insensitive to priors, which means that the negative influence of different priors will be minimized.","abstract_html":"The current study proposes a Bayesian Expectation-Maximization-Maximization (Bayesian EMM, or BEMM), which is an alternative feasible Bayesian algorithm for the three-parameter logistic model (3PLM). The Bayesian EMM takes full advantage of both the EMM and the Bayesian approach. The BEMM not only successfully solves the issue of inaccurate estimates for few items in the EMM algorithm, but also alleviates the negative effect caused by different priors in the traditional Bayesian EM. The simulation studies and real data examples indicate that: (1) The Bayesian EMM can produce more accurate and stable item estimates. (2) Standard errors (SE) yielded by the Bayesian EMM tend to be smaller than the traditional Bayesian EM. (3) The Bayesian EMM is insensitive to priors, which means that the negative influence of different priors will be minimized.","abstract_has_math":false,"creators":["Guo, Shaoyang"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Educational Psychology","degree_department":null,"school":null,"contributors":["Zhang, Jinming","Chang, Hua-Hua","Anderson, Carolyn J."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-08-10T19:51:44Z","date_published":"2017-08-10T19:51:44Z","updated_at":"2026-07-22T22:24:34Z","subjects":["Bayesian Expectation-Maximization-Maximization (BEMM)","Three-parameter logistic model (3PLM)"],"languages":["en"],"rights":["Copyright 2017 Shaoyang Guo"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/97545","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Zhang, Jinming","Chang, Hua-Hua","Anderson, Carolyn J."]},{"key":"dc:creator","label":"Author","values":["Guo, Shaoyang"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2017-08-10T19:51:44Z","2019-08-11T09:15:21Z","2017-04-05","2017-05"]},{"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":["Bayesian Expectation-Maximization-Maximization (BEMM)","Three-parameter logistic model (3PLM)"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2017 Shaoyang Guo"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/97545"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The current study proposes a Bayesian Expectation-Maximization-Maximization (Bayesian EMM, or BEMM), which is an alternative feasible Bayesian algorithm for the three-parameter logistic model (3PLM). The Bayesian EMM takes full advantage of both the EMM and the Bayesian approach. The BEMM not only successfully solves the issue of inaccurate estimates for few items in the EMM algorithm, but also alleviates the negative effect caused by different priors in the traditional Bayesian EM. The simulation studies and real data examples indicate that: (1) The Bayesian EMM can produce more accurate and stable item estimates. (2) Standard errors (SE) yielded by the Bayesian EMM tend to be smaller than the traditional Bayesian EM. (3) The Bayesian EMM is insensitive to priors, which means that the negative influence of different priors will be minimized.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2019-05-01","The student, Shaoyang Guo, accepted the attached license on 2017-04-04 at 16:14.","The student, Shaoyang Guo, submitted this Thesis for approval on 2017-04-04 at 18:14.","This Thesis was approved for publication on 2017-04-05 at 11:26.","DSpace SAF Submission Ingestion Package generated from Vireo submission #10635 on 2017-08-10 at 14:30:05","Made available in DSpace on 2017-08-10T19:51:44Z (GMT). No. of bitstreams: 2 GUO-THESIS-2017.pdf: 4350581 bytes, checksum: ef5398dbafd66513323aa4efa938baae (MD5) LICENSE.txt: 4209 bytes, checksum: 0c5cdb9cf9731e1a3541cb4324873b6a (MD5) Previous issue date: 2017-04-05","Embargo set by: Colleen Fallaw for item 102598 Lift date: 2019-08-10T21:25:30Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Limited Restriction Lifted for Item 102598 on 2019-08-11T09:15:21Z."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Bayesian Expectation-Maximization-Maximization: a latent-mixture-modeling-based Bayesian algorithm for the three-parameter logistic model"]}]}],"canonical_facts":{"dc:contributor":["Zhang, Jinming","Chang, Hua-Hua","Anderson, Carolyn J."],"dc:creator":["Guo, Shaoyang"],"dc:date":["2017-08-10T19:51:44Z","2019-08-11T09:15:21Z","2017-04-05","2017-05"],"dc:description":["The current study proposes a Bayesian Expectation-Maximization-Maximization (Bayesian EMM, or BEMM), which is an alternative feasible Bayesian algorithm for the three-parameter logistic model (3PLM). The Bayesian EMM takes full advantage of both the EMM and the Bayesian approach. The BEMM not only successfully solves the issue of inaccurate estimates for few items in the EMM algorithm, but also alleviates the negative effect caused by different priors in the traditional Bayesian EM. The simulation studies and real data examples indicate that: (1) The Bayesian EMM can produce more accurate and stable item estimates. (2) Standard errors (SE) yielded by the Bayesian EMM tend to be smaller than the traditional Bayesian EM. (3) The Bayesian EMM is insensitive to priors, which means that the negative influence of different priors will be minimized.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2019-05-01","The student, Shaoyang Guo, accepted the attached license on 2017-04-04 at 16:14.","The student, Shaoyang Guo, submitted this Thesis for approval on 2017-04-04 at 18:14.","This Thesis was approved for publication on 2017-04-05 at 11:26.","DSpace SAF Submission Ingestion Package generated from Vireo submission #10635 on 2017-08-10 at 14:30:05","Made available in DSpace on 2017-08-10T19:51:44Z (GMT). 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