Adjusting statistical benchmark risk analysis to account for non-spatial autocorrelation, with application to natural hazard risk assessment

Jingyu Liu, Walter W. Piegorsch, A. Grant Schissler, Rachel R. McCaster, Susan L. Cutter

Research output: Contribution to journalArticlepeer-review

Abstract

We develop and study a quantitative, interdisciplinary strategy for conducting statistical risk analyses within the ‘benchmark risk’ paradigm of contemporary risk assessment when potential autocorrelation exists among sample units. We use the methodology to explore information on vulnerability to natural hazards across 3108 counties in the conterminous 48 US states, applying a place-based resilience index to an existing knowledgebase of hazardous incidents and related human casualties. An extension of a centered autologistic regression model is applied to relate local, county-level vulnerability to hazardous outcomes. Adjustments for autocorrelation embedded in the resiliency information are applied via a novel, non-spatial neighborhood structure. Statistical risk-benchmarking techniques are then incorporated into the modeling framework, wherein levels of high and low vulnerability to hazards are identified.

Original languageEnglish (US)
JournalJournal of Applied Statistics
DOIs
StateAccepted/In press - 2021

Keywords

  • Benchmark dose
  • centered autologistic model
  • maximum pseudo-likelihood
  • natural hazard vulnerability
  • non-spatial autocorrelation
  • quantitative risk assessment

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Fingerprint Dive into the research topics of 'Adjusting statistical benchmark risk analysis to account for non-spatial autocorrelation, with application to natural hazard risk assessment'. Together they form a unique fingerprint.

Cite this