{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/110433"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/110433","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Functional data methods for climatological processes","abstract":"Many climatological and environmental processes take the form of trajectories or surfaces. In the language of Statistics, these observations can be considered as functional data and the tools for studying the behavior of functional data define a framework known as Functional Data Analysis (FDA). In the following Chapters we will propose three FDA methods to model three different climatological phenomena. Chapter 1 will develop a robust test statistic for differentiating between two ensembles of spatial processes. We use this method to test for significant influence of historical proxy observations in paleoclimate reconstructions. Chapter 2 introduces a new class of functional data depths and a rigorous shape outlier detector based on elastic distance. This method handled functional data observed on nonlinear manifolds, such as spheres, which allows us to identify anomalously shaped hurricane trajectories in the Atlantic. Finally, in Chapter 3 we propose a computationally efficient and robust changepoint detector for functional data. We use this to test for, and estimate, changepoints in a long sequence of atmospheric interferometer profile measurements.","abstract_html":"Many climatological and environmental processes take the form of trajectories or surfaces. In the language of Statistics, these observations can be considered as functional data and the tools for studying the behavior of functional data define a framework known as Functional Data Analysis (FDA). In the following Chapters we will propose three FDA methods to model three different climatological phenomena. Chapter 1 will develop a robust test statistic for differentiating between two ensembles of spatial processes. We use this method to test for significant influence of historical proxy observations in paleoclimate reconstructions. Chapter 2 introduces a new class of functional data depths and a rigorous shape outlier detector based on elastic distance. This method handled functional data observed on nonlinear manifolds, such as spheres, which allows us to identify anomalously shaped hurricane trajectories in the Atlantic. Finally, in Chapter 3 we propose a computationally efficient and robust changepoint detector for functional data. We use this to test for, and estimate, changepoints in a long sequence of atmospheric interferometer profile measurements.","abstract_has_math":false,"creators":["Harris, Trevor Austin"],"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","Shao, Xiaofeng","Narisetty, Naveen N","Tucker, James D"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-09-17T01:10:39Z","date_published":"2021-09-17T01:10:39Z","updated_at":"2026-07-22T22:24:50Z","subjects":["functional data analysis","climate science, anomaly detection","paleoclimate"],"languages":["en"],"rights":["Copyright 2021 Trevor Harris"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/110433","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Li, Bo","Shao, Xiaofeng","Narisetty, Naveen N","Tucker, James D"]},{"key":"dc:creator","label":"Author","values":["Harris, Trevor Austin"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2021-09-17T01:10:39Z","2021-04-06","2021-05"]},{"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":["functional data analysis","climate science, anomaly detection","paleoclimate"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2021 Trevor Harris"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/110433"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Many climatological and environmental processes take the form of trajectories or surfaces. In the language of Statistics, these observations can be considered as functional data and the tools for studying the behavior of functional data define a framework known as Functional Data Analysis (FDA). In the following Chapters we will propose three FDA methods to model three different climatological phenomena. Chapter 1 will develop a robust test statistic for differentiating between two ensembles of spatial processes. We use this method to test for significant influence of historical proxy observations in paleoclimate reconstructions. Chapter 2 introduces a new class of functional data depths and a rigorous shape outlier detector based on elastic distance. This method handled functional data observed on nonlinear manifolds, such as spheres, which allows us to identify anomalously shaped hurricane trajectories in the Atlantic. Finally, in Chapter 3 we propose a computationally efficient and robust changepoint detector for functional data. We use this to test for, and estimate, changepoints in a long sequence of atmospheric interferometer profile measurements.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2021-09-16 without embargo terms","The student, Trevor Harris, accepted the attached license on 2021-04-02 at 16:25.","The student, Trevor Harris, submitted this Dissertation for approval on 2021-04-03 at 18:17.","This Dissertation was approved for publication on 2021-04-06 at 15:41.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16229 on 2021-09-16 at 16:40:25","Made available in DSpace on 2021-09-17T01:10:39Z (GMT). No. of bitstreams: 2 HARRIS-DISSERTATION-2021.pdf: 24166483 bytes, checksum: b230d083cef2123d03d99595e502a17f (MD5) LICENSE.txt: 4210 bytes, checksum: 174ca01779ab1f2a0126d2ff4da092a0 (MD5) Previous issue date: 2021-04-06"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Functional data methods for climatological processes"]}]}],"canonical_facts":{"dc:contributor":["Li, Bo","Shao, Xiaofeng","Narisetty, Naveen N","Tucker, James D"],"dc:creator":["Harris, Trevor Austin"],"dc:date":["2021-09-17T01:10:39Z","2021-04-06","2021-05"],"dc:description":["Many climatological and environmental processes take the form of trajectories or surfaces. In the language of Statistics, these observations can be considered as functional data and the tools for studying the behavior of functional data define a framework known as Functional Data Analysis (FDA). In the following Chapters we will propose three FDA methods to model three different climatological phenomena. Chapter 1 will develop a robust test statistic for differentiating between two ensembles of spatial processes. We use this method to test for significant influence of historical proxy observations in paleoclimate reconstructions. Chapter 2 introduces a new class of functional data depths and a rigorous shape outlier detector based on elastic distance. This method handled functional data observed on nonlinear manifolds, such as spheres, which allows us to identify anomalously shaped hurricane trajectories in the Atlantic. Finally, in Chapter 3 we propose a computationally efficient and robust changepoint detector for functional data. We use this to test for, and estimate, changepoints in a long sequence of atmospheric interferometer profile measurements.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2021-09-16 without embargo terms","The student, Trevor Harris, accepted the attached license on 2021-04-02 at 16:25.","The student, Trevor Harris, submitted this Dissertation for approval on 2021-04-03 at 18:17.","This Dissertation was approved for publication on 2021-04-06 at 15:41.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16229 on 2021-09-16 at 16:40:25","Made available in DSpace on 2021-09-17T01:10:39Z (GMT). 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