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Change Point Detection and Spatio-Te...

Tellier, Marie-Agn'es S.

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Change Point Detection and Spatio-Temporal Analysis of Discontinuous Sawtooth Time Series of Regressions Using R-INLA: Examples from the Florida Spiny Lobster Commercial Trap Fishery (1995-2015).

Record Type:	Electronic resources : Monograph/item
Title/Author:	Change Point Detection and Spatio-Temporal Analysis of Discontinuous Sawtooth Time Series of Regressions Using R-INLA: Examples from the Florida Spiny Lobster Commercial Trap Fishery (1995-2015)./
Author:	Tellier, Marie-Agn'es S.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	250 p.
Notes:	Source: Dissertations Abstracts International, Volume: 80-07, Section: B.
Contained By:	Dissertations Abstracts International80-07B.
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13421293
ISBN:	9780438787766

Change Point Detection and Spatio-Temporal Analysis of Discontinuous Sawtooth Time Series of Regressions Using R-INLA: Examples from the Florida Spiny Lobster Commercial Trap Fishery (1995-2015).
Tellier, Marie-Agn'es S.

Change Point Detection and Spatio-Temporal Analysis of Discontinuous Sawtooth Time Series of Regressions Using R-INLA: Examples from the Florida Spiny Lobster Commercial Trap Fishery (1995-2015). - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 250 p.

Source: Dissertations Abstracts International, Volume: 80-07, Section: B.

Thesis (Ph.D.)--University of Wyoming, 2018.

This item must not be added to any third party search indexes.

Time series analysis was developed to address the correlation between consecutive observations that traditional regression ignores. However, what to do when one encounters data that are time series of regressions, a repetition of time series creating a motif? Whether by hunting or fishing, any removal of animals of limited population from their environment is usually regulated by the government in order to preserve stock and future availability, and prevent species extinction. As such, most regulated species undergo a sequence of closed-open sporting seasons creating a pattern of repeated regressions with structural discontinuity. In the presence of such structural discontinuity, could one find and estimate change points, or drastic change in the mean response, in the time series. Moreover, most data collected in ecology often have spatial information attached to them. Thus, in addition to the temporal correlation, one should also model the spatial correlation present in the data. This dissertation introduces the use of R-INLA in (1) estimating model parameters for discontinuous, dynamic, two-scale Bayesian regression models rather than the more traditional but extremely time consuming approach of using MCMC, (2) establishing an adequate method in finding and estimating change points in the mean in such models, and (3) easily extending such Bayesian dynamic model to Spatio-temporal settings. Real life applications and results are showed using spiny lobster (Panulirus argus) commercial trap fishery data collected daily by the State of Florida between 1995 and 2015.

ISBN: 9780438787766Subjects--Topical Terms:

517247
Statistics.

Change Point Detection and Spatio-Temporal Analysis of Discontinuous Sawtooth Time Series of Regressions Using R-INLA: Examples from the Florida Spiny Lobster Commercial Trap Fishery (1995-2015).
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245 1 0 $a Change Point Detection and Spatio-Temporal Analysis of Discontinuous Sawtooth Time Series of Regressions Using R-INLA: Examples from the Florida Spiny Lobster Commercial Trap Fishery (1995-2015).
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300 $a 250 p.
500 $a Source: Dissertations Abstracts International, Volume: 80-07, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Anderson-Sprecher, Richard C.
502 $a Thesis (Ph.D.)--University of Wyoming, 2018.
506 $a This item must not be added to any third party search indexes.
506 $a This item must not be sold to any third party vendors.
520 $a Time series analysis was developed to address the correlation between consecutive observations that traditional regression ignores. However, what to do when one encounters data that are time series of regressions, a repetition of time series creating a motif? Whether by hunting or fishing, any removal of animals of limited population from their environment is usually regulated by the government in order to preserve stock and future availability, and prevent species extinction. As such, most regulated species undergo a sequence of closed-open sporting seasons creating a pattern of repeated regressions with structural discontinuity. In the presence of such structural discontinuity, could one find and estimate change points, or drastic change in the mean response, in the time series. Moreover, most data collected in ecology often have spatial information attached to them. Thus, in addition to the temporal correlation, one should also model the spatial correlation present in the data. This dissertation introduces the use of R-INLA in (1) estimating model parameters for discontinuous, dynamic, two-scale Bayesian regression models rather than the more traditional but extremely time consuming approach of using MCMC, (2) establishing an adequate method in finding and estimating change points in the mean in such models, and (3) easily extending such Bayesian dynamic model to Spatio-temporal settings. Real life applications and results are showed using spiny lobster (Panulirus argus) commercial trap fishery data collected daily by the State of Florida between 1995 and 2015.
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