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The research estimates vector fields from noisy time series and explores neural continuous-time Markov models, linking advanced modeling techniques to practical inference tasks. It also addresses equity in suicide death prediction by using resampling approaches and equity-weighted bootstrapping with fairness metrics, emphasizing how mitigation strategies can improve outcomes for underrepresented groups.","UC Merced  \nUC Merced Electronic Theses and Dissertations  \nTitle  \nMachine Learning and Data Science for System Identification, Public Health, and Equity  \nPermalink  \n[https://escholarship.org/uc/item/54s5h219](https://escholarship.org/uc/item/54s5h219)  \nISBN  \n9798297606630  \nAuthor  \nReeves, Majerle Elizabeth  \nPublication Date  \n2023-07-12  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nUNIVERSITY OF CALIFORNIA, MERCED  \nMachine Learning and Data Science for System Identification, Public Health, and Equity  \nA dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy  \nin  \nApplied Mathematics  \nby  \nMajerle Reeves  \nCommittee in charge:  \nDr. Harish S. Bhat, Chair  \nDr. Sidra Goldman-Mellor  \nDr. Erica Rutter  \nAll chapters © 2023 Majerle Reeves  \nThe dissertation of Majerle Reeves is approved, and it is acceptable in quality and form for publication on microfilm and electronically:  \n(Dr. Sidra Goldman-Mellor)  \n(Dr. Erica Rutter)  \n(Dr. Harish S. Bhat, Chair)  \nUniversity of California, Merced  \n2023  \nTABLE OF CONTENTS  \nSignature Page ........................... iii  \nList [of Figures ............................ vi](of Figures ............................ vi)  \nList of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix  \nAcknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . xii  \nCurriculum Vitae . . . . . . . . . . . . . . . . . . . . . . . . . . xiv  \nAbstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvii  \nChapter 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1  \nChapter 2 Estimating Vector Fields from Noisy Time Series . . . . . . . . 4  \n2. 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 5  \n2.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7  \n2.2.1 Filtering . . . . . . . . . . . . . . . . . . . . . . . . 7  \n2.3 2R2.2e3s.2u1ltsThFiteH.eu.Pagh. r–.amN.aeg.teu.rim.zoa. ti.on.. s.. o.f . . . . . . . . . . . . . .. . . . . . . . . . . . . 799  \n2.3.2 Nonlinear Oscillator Network . . . . . . . . . . . . 14  \n2.3.3 MicroPMU Data . . . . . . . . . . . . . . . . . . . 21  \n2.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . 21  \n2.5 Author Contributions . . . . . . . . . . . . . . . . . . . . . 23  \nChapter 3 Resampling to Address Inequities in Suicide Death Prediction . 24  \n3. 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 25  \n3.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26  \n3.2. 1 Data Sources . . . . . . . . . . . . . . . . . . . . . 26  \n3.2.2 Statistical Analysis . . . . . . . . . . . . . . . . . . 29  \n3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31  \n3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . 35  \n3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . 38  \n3.6 Author Contributions . . . . . . . . . . . . . . . . . . . . . 39  \nChapter 4 Equity-Weighted Bootstrapping: Examples and Analysis . . . . 40  \n4. 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 41  \n4.2 Data, Metrics and Preliminary Modeling . . . . . . . . . . 42  \n4.2. 1 Fairness Metrics . . . . . . . . . . . . . . . . . . . . 44  \n4.2.2 Preliminary Modeling . . . . . . . . . . . . . . . . . 45  \n4.3 Equity-Directed Bootstrap: Method and Results . . . . . . 48  \n4.4 Analysis of the Equity-Directed Bootstrap . . . . . . . . . 52  \n4.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . 61  \nChapter 5 Neural Continuous-Time Markov Models . . . . . . . . . . . . . 63  \n5. 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 64  \n5.2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . 66  \n5.2. 1 Stochastic Reaction Networks . . . . . . . . . . . . 66  \n5.2.2 Maximum Likelihood Estimation . . . . . . . . . . 67  \n5.3 Met","cbCaiqc44pMpaMw3","https://ap.wps.com/l/cbCaiqc44pMpaMw3","pdf",3696513,1,111,"English","en",105,"# Table of Contents\n## Chapter 1 Introduction\n## Chapter 2 Estimating Vector Fields from Noisy Time Series\n## Chapter 3 Resampling to Address Inequities in Suicide Death Prediction\n## Chapter 4 Equity-Weighted Bootstrapping: Examples and Analysis\n## Chapter 5 Neural Continuous-Time Markov Models\n## Chapter 6 Conclusion\n## Bibliography","[{\"question\":\"What are the main research topics of the dissertation?\",\"answer\":\"The dissertation covers machine learning and data science for system identification from noisy data, neural continuous-time Markov modeling, and equity-focused methods in public health prediction tasks.\"},{\"question\":\"How does the work address inequities in suicide death prediction?\",\"answer\":\"It proposes resampling strategies and equity-weighted bootstrapping, evaluating performance with fairness metrics to study and mitigate inequities across groups.\"},{\"question\":\"Which modeling approach is used for system identification?\",\"answer\":\"The research estimates vector fields from noisy time series, combining filtering procedures with ODE learning methods and neural modeling approaches.\"}]","Machine Learning and Data Science for System Identification, Public Health, and Equity - 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