{"id":5303,"date":"2022-07-28T17:27:26","date_gmt":"2022-07-28T11:27:26","guid":{"rendered":"https:\/\/www.isrt.ac.bd\/?post_type=tribe_events&#038;p=5303"},"modified":"2022-07-28T17:27:26","modified_gmt":"2022-07-28T11:27:26","slug":"seminar-on-constrained-inference-in-mixed-models-for-clustered-data","status":"publish","type":"tribe_events","link":"https:\/\/isrt.ac.bd\/event\/seminar-on-constrained-inference-in-mixed-models-for-clustered-data\/","title":{"rendered":"Seminar on &#8220;Constrained inference in mixed models for clustered data&#8221;"},"content":{"rendered":"<div class=\"gmail_quote\">Title: Constrained inference in mixed models for clustered data<\/div>\n<div class=\"gmail_quote\">\n<div class=\"gmail_default\"><\/div>\n<p>Abstract:<br \/>\nMixed models are commonly used for analyzing clustered data, including<br \/>\nlongitudinal data and repeated measurements. Unrestricted full maximum<br \/>\nlikelihood (ML) methods have been extensively studied in the literature<br \/>\nfor analyzing generalized, linear, and mixed models. However, constraints<br \/>\nor parameter orderings may occur in practice, and in such cases, we can<br \/>\nimprove the efficiency of a statistical method by incorporating parameter<br \/>\nconstraints into the ML estimation and hypothesis testing. In this talk, I<br \/>\nwill discuss constrained inference with generalized linear mixed models<br \/>\n(GLMMs) under linear inequality constraints. Methods will be assessed<br \/>\nusing both Monte Carlo simulations and actual survey data from a health<br \/>\nstudy.<\/p><\/div>\n<div><\/div>\n<div><\/div>\n<div>Presenter:<\/div>\n<div>\n<div class=\"gmail_default\">Sanjoy Sinha<\/div>\n<div class=\"gmail_quote\">\n<div dir=\"ltr\">\n<div>\n<div class=\"gmail_quote\">Professor<br \/>\nSchool of Mathematics and Statistics<br \/>\nCarleton Deltin 7 Aviator গেম টাকা ইনকাম, Ottawa, ON, Canada<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div><\/div>\n","protected":false},"excerpt":{"rendered":"<p>Title: Constrained inference in mixed models for clustered data Abstract: Mixed models are commonly used for analyzing clustered data, including longitudinal data and repeated measurements. Unrestricted full maximum likelihood (ML) &#8230; <a class=\"\" href=\"https:\/\/isrt.ac.bd\/event\/seminar-on-constrained-inference-in-mixed-models-for-clustered-data\/\">[ Read More ]<\/a><\/p>\n","protected":false},"author":3,"featured_media":0,"template":"","meta":{"_acf_changed":false,"nf_dc_page":"","_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"_tribe_events_status":"","_tribe_events_status_reason":"","footnotes":""},"tags":[],"tribe_events_cat":[15],"class_list":["post-5303","tribe_events","type-tribe_events","status-publish","hentry","tribe_events_cat-seminar","cat_seminar"],"acf":[],"_links":{"self":[{"href":"https:\/\/isrt.ac.bd\/wp-json\/wp\/v2\/tribe_events\/5303","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/isrt.ac.bd\/wp-json\/wp\/v2\/tribe_events"}],"about":[{"href":"https:\/\/isrt.ac.bd\/wp-json\/wp\/v2\/types\/tribe_events"}],"author":[{"embeddable":true,"href":"https:\/\/isrt.ac.bd\/wp-json\/wp\/v2\/users\/3"}],"version-history":[{"count":2,"href":"https:\/\/isrt.ac.bd\/wp-json\/wp\/v2\/tribe_events\/5303\/revisions"}],"predecessor-version":[{"id":5305,"href":"https:\/\/isrt.ac.bd\/wp-json\/wp\/v2\/tribe_events\/5303\/revisions\/5305"}],"wp:attachment":[{"href":"https:\/\/isrt.ac.bd\/wp-json\/wp\/v2\/media?parent=5303"}],"wp:term":[{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/isrt.ac.bd\/wp-json\/wp\/v2\/tags?post=5303"},{"taxonomy":"tribe_events_cat","embeddable":true,"href":"https:\/\/isrt.ac.bd\/wp-json\/wp\/v2\/tribe_events_cat?post=5303"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}