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DTSTART;TZID=America/Chicago:20240502T130000
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CREATED:20240411T204638Z
LAST-MODIFIED:20240429T133426Z
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SUMMARY:PhD Dissertation Defense: Mr. Russell Latterman
DESCRIPTION:Bayesian Change Point Detection In Segmented Multi-Group Autoregressive Moving-Average Data For The Study Of COVID-19 In Wisconsin\nMr. Russell Latterman\nUniversity of Wisconsin-Milwaukee \nChangepoint detection involves the discovery of abrupt fluctuations in population dynamics over time. We take a Bayesian approach to estimating points in time at which the parameters of an autoregressive moving average (ARMA) change\, applying a Markov Chain Monte Carlo method. We specifically assume that data may originate from one of two groups. We provide estimates of all multi-group parameters of a model of this form for both simulated and real-world data sets. We include a provision to resolve the problem of confounding ARMA parameter estimates and variance of segment data. We apply our model to identify events that may have contributed to the 2020 and 2021 outbreaks of COVID-19 in Waukesha County\, Wisconsin. \nAdvisor: Prof. David Spade \nCommittee Members:\nProfs. Richard Stockbridge\, Istvan Lauko\, Chao Zhu\, and Vytaras Brazauskas
URL:/math/event/phd-dissertation-defense-mr-russell-latterman/
LOCATION:EMS Building\, Room E424A\, E424A; 3200 N Cramer St.\, Milwaukee\, WI\, 53211\, United States
CATEGORIES:Graduate Student Defenses
ORGANIZER;CN="The Department of Mathematical Sciences":MAILTO:math-staff@uwm.edu
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DTSTAMP:20260422T132051
CREATED:20240425T191913Z
LAST-MODIFIED:20240425T192108Z
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SUMMARY:MS Thesis Defense: Mr. Lucas Fellmeth
DESCRIPTION:Utilizing ARMA Models for Non-Independent Replications of Point Processes\nMr. Lucas Fellmeth\nUniversity of Wisconsin-Milwaukee \nThe use of a functional principal component analysis (FPCA) approach for estimating intensity functions from prior work allows us to obtain component scores of replicated point processes under the assumption of independent replications. We show these component scores can be modeled using classical autoregressive moving average (ARMA) models\, thus allowing us to also apply the FPCA model to non-independent replications. The Divvy bike-sharing system in the city of Chicago is showcased as an application. \nAdvisor: Prof. Daniel Gervini \nCommittee Members:\nProfs. Daniel Gervini\, David Spade\, and Chudamani Poudyal
URL:/math/event/ms-thesis-defense-mr-lucas-fellmeth/
LOCATION:EMS Building\, E408\, 3200 N Cramer St\, Milwaukee\, WI\, 53211\, United States
CATEGORIES:Graduate Student Defenses
ORGANIZER;CN="The Department of Mathematical Sciences":MAILTO:math-staff@uwm.edu
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