{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/101317"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/101317","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Statistical inference based on characteristic functions for intractable likelihood problems","abstract":"This dissertation is devoted to statistical inference based on characteristic functions. For some popular stochastic processes (e.g., Lévy processes, Lévy driven Ornstein-Uhlenbeck processes), the transition density may not be available. However, the (conditional) characteristic function is sometimes known. We study various statistical inference methods for fitting those processes with implicit characteristic functions. In the first part, an efficient sampling method based on Bayesian empirical likelihood is developed. The method involves pseudo-marginal Markov chain Monte Carlo with temperature and is shown to be effective for Lévy processes. In the second part and third part, we study maximum likelihood methods and empirical characteristic function estimation based on characteristic functions. We find the analyticity of the characteristic function can make efficient implementations of both methods possible, guaranteeing asymptotic properties as well. We also find, for certain models, very large samples might be needed to accurately identify the true parameters. Numerical results show the appealingness of some infinite activity models. In the last part, this dissertation includes my another project, which is about truth discovery in data mining. A dynamic model is developed to discover the truth between information sources across time. Experiments on real-world applications demonstrate its advantages over previous approaches.","abstract_html":"This dissertation is devoted to statistical inference based on characteristic functions. For some popular stochastic processes (e.g., Lévy processes, Lévy driven Ornstein-Uhlenbeck processes), the transition density may not be available. However, the (conditional) characteristic function is sometimes known. We study various statistical inference methods for fitting those processes with implicit characteristic functions. In the first part, an efficient sampling method based on Bayesian empirical likelihood is developed. The method involves pseudo-marginal Markov chain Monte Carlo with temperature and is shown to be effective for Lévy processes. In the second part and third part, we study maximum likelihood methods and empirical characteristic function estimation based on characteristic functions. We find the analyticity of the characteristic function can make efficient implementations of both methods possible, guaranteeing asymptotic properties as well. We also find, for certain models, very large samples might be needed to accurately identify the true parameters. Numerical results show the appealingness of some infinite activity models. In the last part, this dissertation includes my another project, which is about truth discovery in data mining. A dynamic model is developed to discover the truth between information sources across time. Experiments on real-world applications demonstrate its advantages over previous approaches.","abstract_has_math":false,"creators":["Yang, Fan"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Statistics","degree_department":null,"school":null,"contributors":["Chen, Yuguo","Feng, Liming","Chronopoulou, Alexandra","Shao, Xiaofeng"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-09-04T20:47:14Z","date_published":"2018-09-04T20:47:14Z","updated_at":"2026-07-22T22:24:38Z","subjects":["characteristic functions","statistical inference","Lévy processes","MCMC","trapezoidal rule","asymptotic properties"],"languages":["en"],"rights":["Copyright 2018 Fan Yang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/101317","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Chen, Yuguo","Feng, Liming","Chronopoulou, Alexandra","Shao, Xiaofeng"]},{"key":"dc:creator","label":"Author","values":["Yang, Fan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-09-04T20:47:14Z","2020-09-05T09:15:09Z","2018-04-17","2018-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"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":["characteristic functions","statistical inference","Lévy processes","MCMC","trapezoidal rule","asymptotic properties"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2018 Fan Yang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/101317"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["This dissertation is devoted to statistical inference based on characteristic functions. For some popular stochastic processes (e.g., Lévy processes, Lévy driven Ornstein-Uhlenbeck processes), the transition density may not be available. However, the (conditional) characteristic function is sometimes known. We study various statistical inference methods for fitting those processes with implicit characteristic functions. In the first part, an efficient sampling method based on Bayesian empirical likelihood is developed. The method involves pseudo-marginal Markov chain Monte Carlo with temperature and is shown to be effective for Lévy processes. In the second part and third part, we study maximum likelihood methods and empirical characteristic function estimation based on characteristic functions. We find the analyticity of the characteristic function can make efficient implementations of both methods possible, guaranteeing asymptotic properties as well. We also find, for certain models, very large samples might be needed to accurately identify the true parameters. Numerical results show the appealingness of some infinite activity models. In the last part, this dissertation includes my another project, which is about truth discovery in data mining. A dynamic model is developed to discover the truth between information sources across time. Experiments on real-world applications demonstrate its advantages over previous approaches.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2020-05-01","The student, Fan Yang, accepted the attached license on 2018-04-16 at 11:52.","The student, Fan Yang, submitted this Dissertation for approval on 2018-04-16 at 12:21.","This Dissertation was approved for publication on 2018-04-17 at 08:05.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12252 on 2018-08-31 at 17:29:05","Made available in DSpace on 2018-09-04T20:47:14Z (GMT). No. of bitstreams: 2 YANG-DISSERTATION-2018.pdf: 2201881 bytes, checksum: ec4543c5255e8efbdf4828480e98ec1d (MD5) LICENSE.txt: 4205 bytes, checksum: 427e908a0b11d1b5c497bd73df3f4443 (MD5) Previous issue date: 2018-04-17","Embargo set by: Seth Robbins for item 107402 Lift date: 2020-09-04T20:47:38Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Embargo set by: Seth Robbins for item 107402 Lift date: 2020-09-04T20:50:11Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Limited Restriction Lifted for Item 107402 on 2020-09-05T09:15:09Z."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Statistical inference based on characteristic functions for intractable likelihood problems"]}]}],"canonical_facts":{"dc:contributor":["Chen, Yuguo","Feng, Liming","Chronopoulou, Alexandra","Shao, Xiaofeng"],"dc:creator":["Yang, Fan"],"dc:date":["2018-09-04T20:47:14Z","2020-09-05T09:15:09Z","2018-04-17","2018-05"],"dc:description":["This dissertation is devoted to statistical inference based on characteristic functions. For some popular stochastic processes (e.g., Lévy processes, Lévy driven Ornstein-Uhlenbeck processes), the transition density may not be available. However, the (conditional) characteristic function is sometimes known. We study various statistical inference methods for fitting those processes with implicit characteristic functions. In the first part, an efficient sampling method based on Bayesian empirical likelihood is developed. The method involves pseudo-marginal Markov chain Monte Carlo with temperature and is shown to be effective for Lévy processes. In the second part and third part, we study maximum likelihood methods and empirical characteristic function estimation based on characteristic functions. We find the analyticity of the characteristic function can make efficient implementations of both methods possible, guaranteeing asymptotic properties as well. We also find, for certain models, very large samples might be needed to accurately identify the true parameters. Numerical results show the appealingness of some infinite activity models. In the last part, this dissertation includes my another project, which is about truth discovery in data mining. A dynamic model is developed to discover the truth between information sources across time. Experiments on real-world applications demonstrate its advantages over previous approaches.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2020-05-01","The student, Fan Yang, accepted the attached license on 2018-04-16 at 11:52.","The student, Fan Yang, submitted this Dissertation for approval on 2018-04-16 at 12:21.","This Dissertation was approved for publication on 2018-04-17 at 08:05.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12252 on 2018-08-31 at 17:29:05","Made available in DSpace on 2018-09-04T20:47:14Z (GMT). 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