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Articles

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

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Pages 2349-2369 | Received 07 Jan 2020, Accepted 12 Mar 2021, Published online: 01 Apr 2021

References

  • B.C. Arnold and D.J. Strauss, Pseudolikelihood estimation: Some examples, Sank. Ser. B 53 (1991), pp. 233–243.
  • J.E. Besag, Nearest-neighbour systems and the auto-logistic model for binary data, J. R. Stat. Soc. Ser. B 34 (1972), pp. 75–83.
  • J.E. Besag, Statistical analysis of non-lattice data, J. R. Stat. Soc. Ser. D 24 (1975), pp. 179–195.
  • M. Bee, R. Benedetti, and G. Espa, Spatial models for flood risk assessment, Environmetrics 19 (2008), pp. 725–741.
  • P.R. Berke and T.J. Campanella, Planning for postdisaster resiliency, Ann. Am. Acad. Pol. Soc. Sci.604 (2006), pp. 192–207.
  • E. Budtz-Jørgensen, N. Keiding, and P. Grandjean, Benchmark dose calculation from epidemiological data, Biometrics 57 (2001), pp. 698–706.
  • P.C. Caragea and M.S. Kaiser, Autologistic models with interpretable parameters, J. Agric. Biol. Environ. Stat. 14 (2009), pp. 281–300.
  • K.S. Crump, A new method for determining allowable daily intakes, Toxicol. Sci. 4 (1984), pp. 854–871.
  • K.S. Crump, Calculation of benchmark doses from continuous data, Risk. Anal. 15 (1995), pp. 79–89.
  • N.A.C. Cressie, Statistics for Spatial Data, John Wiley & Sons, New York, 1993.
  • S.L. Cutter, L. Barnes, M. Berry, C. Burton, E. Evans, E. Tate, and J. Webb, A place-based model for understanding community resilience to natural disasters, Glob. Environ. Change. 18 (2008), pp. 598–606.
  • S.L. Cutter, K.D. Ash, and C.T. Emrich, The geographies of community disaster resilience, Glob. Environ. Change. 29 (2014), pp. 65–77.
  • K.A. Dukes, Cronbach's alpha, in Encyclopedia of Biostatistics 2, P. Armitage and T. Colton, eds., John Wiley & Sons, Chichester, 1998, pp. 1026–1028.
  • C. Folke, S. Carpenter, T. Elmqvist, L. Gunderson, C.S. Holling, and B. Walker, Resilience and sustainable development: Building adaptive capacity in a world of transformations, AMBIO: A J. Human Environ. 31 (2002), pp. 437–440.
  • G.G. Gurney, J. Blythe, H. Adams, W.N. Adger, M. Curnock, L. Faulkner, T. James, and N.A. Marshall, Redefining community based on place attachment in a connected world, Proc. Natl. Acad. Sci. USA 114 (2017), pp. 10077–10082.
  • C. Hardouin, A variational method for parameter estimation in a logistic spatial regression, Spat. Stat. 31 (2019). Article No. 100365 (14 pp.).
  • C. Harvey, Extreme weather events could worsen climate change, Scientific American E&E News (24 January 2019). Available at https://www.scientificamerican.com/article/extreme-weather-events-could-worsen-climate-change/.
  • D.J. Hand, G. Blunt, M.G. Kelly, and N.M. Adams, Data mining for fun and profit, Stat. Sci. 15 (2000), pp. 111–131.
  • J.A. Hoeting, M. Leecaster, and D. Bowden, An improved model for spatially correlated binary responses, J. Agric. Biol. Environ. Stat. 5 (2000), pp. 102–114.
  • C.S. Holling, Resilience and stability of ecological systems, Annu. Rev. Ecol. Syst. 4 (1973), pp. 1–23.
  • D.W. Hosmer, S. Lemeshow, and R.X. Sturdivant, Applied Logistic Regression, 3rd ed., John Wiley & Sons, New York, 2013.
  • J. Hughes, ngspatial: A package for fitting the centered autologistic and sparse spatial generalized linear mixed models for areal data, R. J. 6 (2014), pp. 81–95.
  • J. Hughes, M. Haran, and P.C. Caragea, Autologistic models for binary data on a lattice, Environmetrics 22 (2011), pp. 857–871.
  • R.J.T. Klein, R.J. Nicholls, and F. Thomalla, Resilience to natural hazards: How useful is this concept?Global Environ. Change Part B: Environ. Haz. 5 (2003), pp. 35–45.
  • E.D. Kolaczyk and G. Csárdi, Statistical Analysis of Network Data with R, Springer, New York, 2014.
  • J. Liu, Autologistic modeling in benchmark risk analysis, Ph.D. thesis, Interdisciplinary Program in Statistics, University of Arizona, Tucson, AZ, 2017.
  • J. Liu, W.W. Piegorsch, A.G. Schissler, and S.L. Cutter, Autologistic models for benchmark risk or vulnerability assessment of urban terrorism outcomes, J. R. Stat. Soc. Ser. A 181 (2018), pp. 803–823.
  • S.B. Manyena, The concept of resilience revisited, Disasters 30 (2006), pp. 434–450.
  • C.R. Martin and E. Savage-McGlynn, A ‘good practice’ guide for the reporting of design and analysis for psychometric evaluation, J. Reprod. Infant Psychol. 31 (2013), pp. 449–455.
  • M. Nardo, M. Saisana, A. Saltelli, and S. Tarantola, Handbook on Constructing Composite Indicators: Methodology and User Guide, Organisation For Economic Co-Operation and Development Publishing, Paris, 2008.
  • D.K. Nitcheva, W.W. Piegorsch, R.W. West, and R.L. Kodell, Multiplicity-adjusted inferences in risk assessment: Benchmark analysis with quantal response data, Biometrics 61 (2005), pp. 277–286.
  • W.W. Piegorsch and A.J. Bailer, Analyzing Environmental Data, John Wiley & Sons, Chichester, 2005.
  • W.W. Piegorsch, L. An, A. Wickens, W. West, E.A. Peña, and W. Wu, Information-theoretic model-averaged benchmark dose analysis in environmental risk assessment, Environmetrics 24 (2013), pp. 143–157.
  • R Core Team, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, 2019. Available at http://www.R-project.org/.
  • D.W. Scott, Multivariate Density Estimation. Theory, Practice, and Visualization, John Wiley & Sons, New York, 1992.
  • E.C. Tarabusi and G. Guarini, An unbalance adjustment method for development indicators, Soc. Indic. Res. 112 (2013), pp. 19–45.
  • C. Varin, N. Reid, and D. Firth, An overview of composite likelihood methods, Stat. Sin. 21 (2011), pp. 5–42.
  • M.M. Webber, Order in diversity: Community without propinquity, in Cities and Space, L. Wirigo, ed., Johns Hopkins University Press, Baltimore, 1983, pp. 23–56.
  • Y. Zheng and J. Zhu, Markov chain Monte Carlo for a spatial-temporal autologistic regression model, J. Comput. Graph. Stat. 17 (2008), pp. 123–137.

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