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Forecasting earthquakes and earthquake risk

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  • Vere-Jones, David

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  • Vere-Jones, David, 1995. "Forecasting earthquakes and earthquake risk," International Journal of Forecasting, Elsevier, vol. 11(4), pages 503-538, December.
    • Handle: RePEc:eee:intfor:v:11:y:1995:i:4:p:503-538
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    References listed on IDEAS

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    1. Ogata, Y. & Vere-Jones, D., 1984. "Inference for earthquake models: A self-correcting model," Stochastic Processes and their Applications, Elsevier, vol. 17(2), pages 337-347, July.
    2. F. Musmeci & D. Vere-Jones, 1992. "A space-time clustering model for historical earthquakes," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 44(1), pages 1-11, March.
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    Cited by:

    1. Renata Rotondi & Elisa Varini, 2003. "Bayesian analysis of a marked point process: Application in seismic hazard assessment," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 12(1), pages 79-92, February.
    2. Roelof Helmers & Ričardas Zitikis, 1999. "On Estimation of Poisson Intensity Functions," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 51(2), pages 265-280, June.
    3. Ericsson, Neil R., 2017. "How biased are U.S. government forecasts of the federal debt?," International Journal of Forecasting, Elsevier, vol. 33(2), pages 543-559.
    4. Tsai, Chung-Hung & Chen, Cheng-Wu, 2011. "The establishment of a rapid natural disaster risk assessment model for the tourism industry," Tourism Management, Elsevier, vol. 32(1), pages 158-171.
    5. Tsai, Chung-Hung & Chen, Cheng-Wu, 2010. "An earthquake disaster management mechanism based on risk assessment information for the tourism industry-a case study from the island of Taiwan," Tourism Management, Elsevier, vol. 31(4), pages 470-481.
    6. Luca La Rocca, 2008. "Bayesian Non‐Parametric Estimation of Smooth Hazard Rates for Seismic Hazard Assessment," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(3), pages 524-539, September.
    7. Schneider, Michael & Lillo, Fabrizio & Pelizzon, Loriana, 2016. "How has sovereign bond market liquidity changed? An illiquidity spillover analysis," SAFE Working Paper Series 151, Leibniz Institute for Financial Research SAFE.
    8. Chenlong Li & Zhanjie Song & Wenjun Wang, 2020. "Space–time inhomogeneous background intensity estimators for semi-parametric space–time self-exciting point process models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(4), pages 945-967, August.
    9. Samuel N. Cohen & Robert J. Elliott, 2013. "Filters and smoothers for self-exciting Markov modulated counting processes," Papers 1311.6257, arXiv.org.
    10. Altay, Nezih & Narayanan, Arunachalam, 2022. "Forecasting in humanitarian operations: Literature review and research needs," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1234-1244.
    11. Nikolopoulos, Konstantinos & Petropoulos, Fotios & Rodrigues, Vasco Sanchez & Pettit, Stephen & Beresford, Anthony, 2022. "A disaster response model driven by spatial–temporal forecasts," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1214-1220.
    12. Bent Natvig & Ingunn Fride Tvete, 2007. "Bayesian Hierarchical Space–time Modeling of Earthquake Data," Methodology and Computing in Applied Probability, Springer, vol. 9(1), pages 89-114, March.

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