{"id":188,"date":"2013-09-30T20:39:21","date_gmt":"2013-09-30T20:39:21","guid":{"rendered":"http:\/\/pages.charlotte.edu\/colloquium\/?p=188"},"modified":"2022-09-10T22:23:09","modified_gmt":"2022-09-10T22:23:09","slug":"friday-nov-1-at-200pm-in-the-conference-room","status":"publish","type":"post","link":"https:\/\/pages.charlotte.edu\/colloquium\/blog\/2013\/09\/30\/friday-nov-1-at-200pm-in-the-conference-room\/","title":{"rendered":"Friday, Nov 1 at 2:00pm in the conference room"},"content":{"rendered":"<table>\n<tbody>\n<tr>\n<td><a href=\"http:\/\/www.stat.illinois.edu\/people\/faculty\/qu.shtml\">\u00a0Annie (Peiyong) Qu<\/a>, University of Illinois, Urbana-Champaign<\/td>\n<\/tr>\n<tr>\n<td>Title: Time-varying networks estimation and dynamic model selection<\/td>\n<\/tr>\n<tr>\n<td>\nAbstract:\u00a0 In many \u00a0biomedical and social science studies, it is very important to\u00a0identify and predict the dynamic changes of associations among network data over time.\u00a0We propose a varying-coefficient model\u00a0 to incorporate time-varying network data, \u00a0and impose a piecewise penalty\u00a0function to capture local features of the network associations. The advantages of the proposed approach are that it is nonparametric and therefore flexible in modeling dynamic changes of association for network data problems, and capable of identifying the time regions when dynamic changes of associations occur.\u00a0To achieve local sparsity of network estimation, we implement a group penalization strategy involving overlapping parameters among different groups. However, this imposes \u00a0great challenges \u00a0in the optimization process for handling large-dimensional network data observed at many time points. We develop a fast algorithm, based on the smoothing proximal gradient method, which is computationally efficient and accurate.\u00a0We illustrate the proposed method through simulation studies \u00a0and children&#8217;s attention deficit hyperactivity disorder fMRI data, and \u00a0show that the proposed\u00a0method and algorithm efficiently recover the dynamic network changes over time. The proposed approach works especially well when networks are sparse. This is joint work with Xinxin Shu.<\/p>\n<div><\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u00a0Annie (Peiyong) Qu, University of Illinois, Urbana-Champaign Title: Time-varying networks estimation and dynamic model selection Abstract:\u00a0 In many \u00a0biomedical and social science studies, it is very important to\u00a0identify and predict the dynamic changes of associations among network data over time.\u00a0We propose a varying-coefficient model\u00a0 to incorporate time-varying network data, \u00a0and impose a piecewise penalty\u00a0function to [&hellip;]<\/p>\n","protected":false},"author":16,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[8],"tags":[],"class_list":["post-188","post","type-post","status-publish","format-standard","hentry","category-past-talks"],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_shortlink":"https:\/\/wp.me\/p3kCtT-32","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/pages.charlotte.edu\/colloquium\/wp-json\/wp\/v2\/posts\/188","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/pages.charlotte.edu\/colloquium\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/pages.charlotte.edu\/colloquium\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/pages.charlotte.edu\/colloquium\/wp-json\/wp\/v2\/users\/16"}],"replies":[{"embeddable":true,"href":"https:\/\/pages.charlotte.edu\/colloquium\/wp-json\/wp\/v2\/comments?post=188"}],"version-history":[{"count":1,"href":"https:\/\/pages.charlotte.edu\/colloquium\/wp-json\/wp\/v2\/posts\/188\/revisions"}],"predecessor-version":[{"id":189,"href":"https:\/\/pages.charlotte.edu\/colloquium\/wp-json\/wp\/v2\/posts\/188\/revisions\/189"}],"wp:attachment":[{"href":"https:\/\/pages.charlotte.edu\/colloquium\/wp-json\/wp\/v2\/media?parent=188"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/pages.charlotte.edu\/colloquium\/wp-json\/wp\/v2\/categories?post=188"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/pages.charlotte.edu\/colloquium\/wp-json\/wp\/v2\/tags?post=188"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}