{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/120532"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/120532","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Changepoint detection and estimation for spatially indexed functional time series","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-05-01","abstract_has_math":false,"creators":["Wang, Mengchen"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Statistics","degree_department":null,"school":null,"contributors":["Li, Bo","Harris, Trevor","Shao, Xiaofeng","Park, Trevor"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-05","date_published":"2023-05","updated_at":"2026-07-22T22:24:57Z","subjects":["Bayesian Hierarchical Model","Changepoint","Functional Data","Spatial Correlation"],"languages":["en","eng"],"rights":["Copyright 2023 Mengchen Wang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/120532","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Li, Bo","Harris, Trevor","Shao, Xiaofeng","Park, Trevor"]},{"key":"dc:creator","label":"Author","values":["Wang, Mengchen"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-05","2023-04-23"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Statistics"]},{"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":["Bayesian Hierarchical Model","Changepoint","Functional Data","Spatial Correlation"]}]},{"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 2023 Mengchen Wang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/120532"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-05-01","The student, Mengchen Wang, accepted the attached license on 2023-04-19 at 20:59.","The student, Mengchen Wang, submitted this Dissertation for approval on 2023-04-19 at 21:11.","This Dissertation was approved for publication on 2023-04-23 at 11:20.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19056 on 2023-09-01 at 17:21:05","We develop mean-based asynchronous changepoints estimation and detection methods for spatially indexed functional time series in the Bayesian framework. Chapter 2 proposes to build spatially correlated two-piece linear models with appropriate variance structure for modeling a process based on the cumulative sum (CUSUM) statistic. The method allows spatially varying changepoints and exploits spatial correlations to improve estimation. We perform comprehensive simulations to explore settings with weak or strong spatial correlation and weak or strong change signals. The results indicate that our method provides better estimation accuracy and uncertainty quantification compared to existing functional changepoint estimators. We demonstrate our method using a temperature data set and a coronavirus disease 2019 study. In Chapter 3, we develop a compact approach by combining detection and estimation in a single Bayesian hierarchical model. We further enhance the estimation accuracy using a quadratic fit and introduce a parameter directly describing the magnitude of the change signal. We employ a continuous spike-and-slab prior with a mixture of two truncated normal distributions for the change magnitude parameter to detect changepoint. Extensive simulations demonstrate that our method works well for various scenarios including data with different proportions of null locations, different strengths of spatial correlations, and different sizes of change signals. We apply our method to analyze the influence of the June 15th, 1991 eruption of Mount Pinatubo on global temperatures. We detect changepoints in a spatially smoothing pattern, where regions at similar latitudes exhibit similar changepoints. The results show evidence of the early impact of the eruption on low-latitude regions, later impact on high latitudes, and little impact on the southern hemisphere."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Changepoint detection and estimation for spatially indexed functional time series"]}]}],"canonical_facts":{"dc:contributor":["Li, Bo","Harris, Trevor","Shao, Xiaofeng","Park, Trevor"],"dc:creator":["Wang, Mengchen"],"dc:date":["2023-05","2023-04-23"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-05-01","The student, Mengchen Wang, accepted the attached license on 2023-04-19 at 20:59.","The student, Mengchen Wang, submitted this Dissertation for approval on 2023-04-19 at 21:11.","This Dissertation was approved for publication on 2023-04-23 at 11:20.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19056 on 2023-09-01 at 17:21:05","We develop mean-based asynchronous changepoints estimation and detection methods for spatially indexed functional time series in the Bayesian framework. Chapter 2 proposes to build spatially correlated two-piece linear models with appropriate variance structure for modeling a process based on the cumulative sum (CUSUM) statistic. The method allows spatially varying changepoints and exploits spatial correlations to improve estimation. We perform comprehensive simulations to explore settings with weak or strong spatial correlation and weak or strong change signals. The results indicate that our method provides better estimation accuracy and uncertainty quantification compared to existing functional changepoint estimators. We demonstrate our method using a temperature data set and a coronavirus disease 2019 study. In Chapter 3, we develop a compact approach by combining detection and estimation in a single Bayesian hierarchical model. We further enhance the estimation accuracy using a quadratic fit and introduce a parameter directly describing the magnitude of the change signal. We employ a continuous spike-and-slab prior with a mixture of two truncated normal distributions for the change magnitude parameter to detect changepoint. Extensive simulations demonstrate that our method works well for various scenarios including data with different proportions of null locations, different strengths of spatial correlations, and different sizes of change signals. We apply our method to analyze the influence of the June 15th, 1991 eruption of Mount Pinatubo on global temperatures. We detect changepoints in a spatially smoothing pattern, where regions at similar latitudes exhibit similar changepoints. The results show evidence of the early impact of the eruption on low-latitude regions, later impact on high latitudes, and little impact on the southern hemisphere."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/120532"],"dc:language":["en","eng"],"dc:rights":["Copyright 2023 Mengchen Wang"],"dc:subject":["Bayesian Hierarchical Model","Changepoint","Functional Data","Spatial Correlation"],"dc:title":["Changepoint detection and estimation for spatially indexed functional time series"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Statistics"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:57Z"}