{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/127141"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/127141","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Graphical models for high-dimensional stochastic processes: Estimation and inference","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-03-28 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-03-28 without embargo terms","abstract_has_math":false,"creators":["Tsai, Katherine"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Koyejo, Sanmi","Kolar, Mladen","Raginsky, Maxim","Srikant, Rayadurgam","Shomorony, Ilan"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-12","date_published":"2024-12","updated_at":"2026-07-22T22:25:03Z","subjects":["Stochastic Process","Network Estimation","Integrative Analysis","Distribution Shift"],"languages":["en","eng"],"rights":["Copyright 2024 Katherine Tsai"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/127141","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Koyejo, Sanmi","Kolar, Mladen","Raginsky, Maxim","Srikant, Rayadurgam","Shomorony, Ilan"]},{"key":"dc:creator","label":"Author","values":["Tsai, Katherine"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-12","2024-10-01"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"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":["Stochastic Process","Network Estimation","Integrative Analysis","Distribution Shift"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2024 Katherine Tsai"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/127141"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-03-28 without embargo terms","The student, Katherine Tsai, accepted the attached license on 2024-08-20 at 11:38.","The student, Katherine Tsai, submitted this Dissertation for approval on 2024-08-20 at 11:53.","This Dissertation was approved for publication on 2024-10-01 at 16:04.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21197 on 2025-03-28 at 14:24:55","Using tools to extract knowledge from data is one of the most essential practices in academia and industry. Conversely, a graphical model represents the distribution of data by a graph – a flexible yet efficient tool to drive insights into potentially complex and high-dimensional systems. It has, therefore, been widely applied to many disciplines, including but not limited to artificial intelligence, biology, social science, and finance. Data, oftentimes, are collected sequentially or in a non-i.i.d. fashion. Such scenarios pose challenges in obtaining faithful estimates and establishing statistical guarantees. However, when data exhibit temporal structure, recovering underlying graphs becomes possible. This thesis is motivated by the pressing needs to develop methodologies to understand complex biological systems like brain networks from neuroimaging with guarantees. It develops methodologies to estimate both directed and undirected graphs from potentially nonlinear and nonstationary multivariate stochastic processes. It also presents novel approaches to make adaptive inferences under changing environments, which can be applied to adjust any statistical or machine learning model in the deployment phase. In terms of theory, we focus on graph recovery, convergence guarantees, statistical error bounds, and identification of the distributions."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Graphical models for high-dimensional stochastic processes: Estimation and inference"]}]}],"canonical_facts":{"dc:contributor":["Koyejo, Sanmi","Kolar, Mladen","Raginsky, Maxim","Srikant, Rayadurgam","Shomorony, Ilan"],"dc:creator":["Tsai, Katherine"],"dc:date":["2024-12","2024-10-01"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-03-28 without embargo terms","The student, Katherine Tsai, accepted the attached license on 2024-08-20 at 11:38.","The student, Katherine Tsai, submitted this Dissertation for approval on 2024-08-20 at 11:53.","This Dissertation was approved for publication on 2024-10-01 at 16:04.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21197 on 2025-03-28 at 14:24:55","Using tools to extract knowledge from data is one of the most essential practices in academia and industry. Conversely, a graphical model represents the distribution of data by a graph – a flexible yet efficient tool to drive insights into potentially complex and high-dimensional systems. It has, therefore, been widely applied to many disciplines, including but not limited to artificial intelligence, biology, social science, and finance. Data, oftentimes, are collected sequentially or in a non-i.i.d. fashion. Such scenarios pose challenges in obtaining faithful estimates and establishing statistical guarantees. However, when data exhibit temporal structure, recovering underlying graphs becomes possible. This thesis is motivated by the pressing needs to develop methodologies to understand complex biological systems like brain networks from neuroimaging with guarantees. It develops methodologies to estimate both directed and undirected graphs from potentially nonlinear and nonstationary multivariate stochastic processes. It also presents novel approaches to make adaptive inferences under changing environments, which can be applied to adjust any statistical or machine learning model in the deployment phase. In terms of theory, we focus on graph recovery, convergence guarantees, statistical error bounds, and identification of the distributions."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/127141"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Katherine Tsai"],"dc:subject":["Stochastic Process","Network Estimation","Integrative Analysis","Distribution Shift"],"dc:title":["Graphical models for high-dimensional stochastic processes: Estimation and inference"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:03Z"}