{"id":{"repo_id":"uwo","oai_identifier":"oai:uwo.scholaris.ca:20.500.14721/18744"},"canonical_url":"https://search.dev.ndltd.org/etd/uwo/oai:uwo.scholaris.ca:20.500.14721/18744","repository":{"repo_id":"uwo","name":"Western University","base_url":"https://uwo.scholaris.ca/server/oai/request"},"display":{"title":"Application of the EM Algorithm for Mixture Models","abstract":"A developmental trajectory describes the course of behaviour over time. Iden­ tifying multiple trajectories within an overall developmental process permits a focus on subgroups of particular interest. This research introduces a SAS macro program that identifies trajectories by using the Expectation-Maximization (EM) algorithm to fit semi-parametric mixtures of logistic distributions to longitudinal binary data. For performance comparison, we consider full maximization algo­ rithms (e.g. SAS procedure PROC TRAJ) and standard EM, as well as two other EM-based algorithms for speeding up convergence. The simulation study shows that our EM methods produce more accurate parameter estimates than the full maximization methods. The EM-based methodology is illustrated with a longitudinal data set involving adolescents smoking behaviours.","abstract_html":"A developmental trajectory describes the course of behaviour over time. Iden­ tifying multiple trajectories within an overall developmental process permits a focus on subgroups of particular interest. This research introduces a SAS macro program that identifies trajectories by using the Expectation-Maximization (EM) algorithm to fit semi-parametric mixtures of logistic distributions to longitudinal binary data. For performance comparison, we consider full maximization algo­ rithms (e.g. SAS procedure PROC TRAJ) and standard EM, as well as two other EM-based algorithms for speeding up convergence. The simulation study shows that our EM methods produce more accurate parameter estimates than the full maximization methods. The EM-based methodology is illustrated with a longitudinal data set involving adolescents smoking behaviours.","abstract_has_math":false,"creators":["Chu, Man-Kee Maggie"],"institution":null,"degree_name":"M Sc","degree_level":null,"degree_discipline":"Epidemiology and Biostatistics","degree_department":null,"school":null,"contributors":[],"advisors":["Koval, John"],"committee_chairs":[],"committee_members":[],"year":2010,"date_issued":"2010-01-01","date_published":"2010-01-01","updated_at":"2026-07-27T21:56:20Z","subjects":["Expectation-Maximization algorithm","Mixture models","Binary data","Longitudinal trajectories"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/20.500.14721/18744","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Koval, John"]},{"key":"dc:creator","label":"Author","values":["Chu, Man-Kee Maggie"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-06-25T18:53:28Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-06-25T18:53:28Z"]},{"key":"dc:date.issued","label":"Date","values":["2010-01-01"]},{"key":"dc:type","label":"Dc Type","values":["thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Epidemiology and Biostatistics"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M Sc"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Expectation-Maximization algorithm","Mixture models","Binary data","Longitudinal trajectories"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/20.500.14721/18744"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["A developmental trajectory describes the course of behaviour over time. Iden­ tifying multiple trajectories within an overall developmental process permits a focus on subgroups of particular interest. This research introduces a SAS macro program that identifies trajectories by using the Expectation-Maximization (EM) algorithm to fit semi-parametric mixtures of logistic distributions to longitudinal binary data. For performance comparison, we consider full maximization algo­ rithms (e.g. SAS procedure PROC TRAJ) and standard EM, as well as two other EM-based algorithms for speeding up convergence. The simulation study shows that our EM methods produce more accurate parameter estimates than the full maximization methods. The EM-based methodology is illustrated with a longitudinal data set involving adolescents smoking behaviours."]},{"key":"dc:title","label":"Title","values":["Application of the EM Algorithm for Mixture Models"]}]}],"canonical_facts":{"dc:contributor.advisor":["Koval, John"],"dc:creator":["Chu, Man-Kee Maggie"],"dc:date.accessioned":["2025-06-25T18:53:28Z"],"dc:date.available":["2025-06-25T18:53:28Z"],"dc:date.issued":["2010-01-01"],"dc:description.abstract":["A developmental trajectory describes the course of behaviour over time. Iden­ tifying multiple trajectories within an overall developmental process permits a focus on subgroups of particular interest. This research introduces a SAS macro program that identifies trajectories by using the Expectation-Maximization (EM) algorithm to fit semi-parametric mixtures of logistic distributions to longitudinal binary data. 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