Persistent Link:
http://hdl.handle.net/10150/626166
Title:
Autologistic Modeling in Benchmark Risk Analysis
Author:
Liu, Jingyu
Issue Date:
2017
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
An important objective in statistical risk assessment is estimation of minimum exposure levels, called Benchmark Doses (BMDs), that induce a pre-specified Benchmark Response (BMR) in a target population. Established inferential approaches for BMD analysis typically involve one-sided, frequentist confidence limits, leading in practice to what are called Benchmark Dose Lower Limits (BMDLs). With this context, a quantitative methodology is developed to characterize vulnerability among 132 U.S. urban centers ('cities') to terrorist events, applying a place-based vulnerability index to a database of terrorist incidents and related human casualties. A centered autologistic regression model is employed to relate urban vulnerability to terrorist outcomes and also to adjust for autocorrelation in the geospatial data. Risk- analytic BMDs are then estimated from this modeling framework, wherein levels of high and low urban vulnerability to terrorism are identified. This new, translational adaptation of the risk-benchmark approach, including its ability to account for geospatial autocorrelation, is seen to operate quite flexibly in this socio-geographic setting. Further, alternative definitions for neighborhoods are considered to extend the autologistic benchmark paradigm to non-spatial settings. All 3108 counties in the contiguous 48 U.S. states are studied to identify a benchmark dose variable as the number of hazards. This is employed to benchmark billion-dollar losses across each county. County-level resilience is used as a potential characteristic for defining the neighborhood structure within the autologistic model.
Type:
text; Electronic Dissertation
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Statistics
Degree Grantor:
University of Arizona
Advisor:
Piegorsch, Walter W.

Full metadata record

DC FieldValue Language
dc.language.isoen_USen
dc.titleAutologistic Modeling in Benchmark Risk Analysisen_US
dc.creatorLiu, Jingyuen
dc.contributor.authorLiu, Jingyuen
dc.date.issued2017-
dc.publisherThe University of Arizona.en
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en
dc.description.abstractAn important objective in statistical risk assessment is estimation of minimum exposure levels, called Benchmark Doses (BMDs), that induce a pre-specified Benchmark Response (BMR) in a target population. Established inferential approaches for BMD analysis typically involve one-sided, frequentist confidence limits, leading in practice to what are called Benchmark Dose Lower Limits (BMDLs). With this context, a quantitative methodology is developed to characterize vulnerability among 132 U.S. urban centers ('cities') to terrorist events, applying a place-based vulnerability index to a database of terrorist incidents and related human casualties. A centered autologistic regression model is employed to relate urban vulnerability to terrorist outcomes and also to adjust for autocorrelation in the geospatial data. Risk- analytic BMDs are then estimated from this modeling framework, wherein levels of high and low urban vulnerability to terrorism are identified. This new, translational adaptation of the risk-benchmark approach, including its ability to account for geospatial autocorrelation, is seen to operate quite flexibly in this socio-geographic setting. Further, alternative definitions for neighborhoods are considered to extend the autologistic benchmark paradigm to non-spatial settings. All 3108 counties in the contiguous 48 U.S. states are studied to identify a benchmark dose variable as the number of hazards. This is employed to benchmark billion-dollar losses across each county. County-level resilience is used as a potential characteristic for defining the neighborhood structure within the autologistic model.en
dc.typetexten
dc.typeElectronic Dissertationen
thesis.degree.namePh.D.en
thesis.degree.leveldoctoralen
thesis.degree.disciplineGraduate Collegeen
thesis.degree.disciplineStatisticsen
thesis.degree.grantorUniversity of Arizonaen
dc.contributor.advisorPiegorsch, Walter W.en
dc.contributor.committeememberPiegorsch, Walter W.en
dc.contributor.committeememberHu, Chengchengen
dc.contributor.committeememberWatkins, Joseph W.en
dc.contributor.committeememberZhang, Hao H.en
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