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Bayesian Change Point Detection in S...

Latterman, Russell,

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Bayesian Change Point Detection in Segmented Multi-Group Autoregressive Moving-average Data for the Study of COVID-19 in Wisconsin /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Bayesian Change Point Detection in Segmented Multi-Group Autoregressive Moving-average Data for the Study of COVID-19 in Wisconsin // Russell Latterman.
Author:	Latterman, Russell,
Description:	1 electronic resource (197 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
Contained By:	Dissertations Abstracts International85-11B.
Subject:	Mathematics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31294479
ISBN:	9798382727059

Bayesian Change Point Detection in Segmented Multi-Group Autoregressive Moving-average Data for the Study of COVID-19 in Wisconsin /
Latterman, Russell,

Bayesian Change Point Detection in Segmented Multi-Group Autoregressive Moving-average Data for the Study of COVID-19 in Wisconsin /Russell Latterman. - 1 electronic resource (197 pages)

Source: Dissertations Abstracts International, Volume: 85-11, Section: B.

Changepoint 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 points in time at which influential events affecting 2020 and 2021 outbreaks of COVID-19 in Waukesha County, Wisconsin, may have occurred.

English

ISBN: 9798382727059Subjects--Topical Terms:

515831
Mathematics.
Subjects--Index Terms:

Autoregressive moving average

Bayesian Change Point Detection in Segmented Multi-Group Autoregressive Moving-average Data for the Study of COVID-19 in Wisconsin /
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245 1 0 $a Bayesian Change Point Detection in Segmented Multi-Group Autoregressive Moving-average Data for the Study of COVID-19 in Wisconsin / $c Russell Latterman.
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300 $a 1 electronic resource (197 pages)
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500 $a Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
500 $a Advisors: Spade, David Committee members: Stockbridge, Richard; Lauko, Istvan; Brazauskas, Vytaras; Zhu, Chao.
502 $b Ph.D. $c The University of Wisconsin - Milwaukee $d 2024.
520 $a Changepoint 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 points in time at which influential events affecting 2020 and 2021 outbreaks of COVID-19 in Waukesha County, Wisconsin, may have occurred.
546 $a English
590 $a School code: 0263
650 4 $a Mathematics. $3 515831
650 4 $a Statistics. $3 517247
650 4 $a Theoretical mathematics. $3 3173530
653 $a Autoregressive moving average
653 $a Bayesian approach
653 $a Changepoint detection
653 $a COVID-19
653 $a Gibbs sampler
653 $a Well log data
690 $a 0405
690 $a 0463
690 $a 0642
710 2 $a The University of Wisconsin - Milwaukee. $b Mathematics. $e degree granting institution. $3 3816305
720 1 $a Spade, David $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 85-11B.
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791 $a Ph.D.
792 $a 2024
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