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Alarm Systems and Catastrophes from a Diverse Point of View

Author

Listed:
  • Pasquale Cirillo

    (University of Bern)

  • Jürg Hüsler

    (University of Bern)

  • Pietro Muliere

    (Bocconi University)

Abstract

Using a chain of urns, we build a Bayesian nonparametric alarm system to predict catastrophic events, such as epidemics, black outs, etc. Differently from other alarm systems in the literature, our model is constantly updated on the basis of the available information, according to the Bayesian paradigm. The papers contains both theoretical and empirical results. In particular, we test our alarm system on a well-known time series of sunspots.

Suggested Citation

  • Pasquale Cirillo & Jürg Hüsler & Pietro Muliere, 2013. "Alarm Systems and Catastrophes from a Diverse Point of View," Methodology and Computing in Applied Probability, Springer, vol. 15(4), pages 821-839, December.
    • Handle: RePEc:spr:metcap:v:15:y:2013:i:4:d:10.1007_s11009-012-9281-z
    DOI: 10.1007/s11009-012-9281-z
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    References listed on IDEAS

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    1. Mezzetti, Maura & Muliere, Pietro & Bulla, Paolo, 2007. "An application of reinforced urn processes to determining maximum tolerated dose," Statistics & Probability Letters, Elsevier, vol. 77(7), pages 740-747, April.
    2. Turkman, M. A. Amaral & Turkman, K. F., 1990. "Optimal alarm systems for autoregressive processes : A Bayesian approach," Computational Statistics & Data Analysis, Elsevier, vol. 10(3), pages 307-314, December.
    3. Pasquale Cirillo & Jürg Hüsler & Pietro Muliere, 2010. "A Nonparametric Urn-Based Approach To Interacting Failing Systems With An Application To Credit Risk Modeling," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 13(08), pages 1223-1240.
    4. Brannas Kurt & Nordstrom Jonas, 2004. "An Integer-Valued Time Series Model for Hotels that Accounts for Constrained Capacity," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 8(4), pages 1-11, December.
    5. Cirillo, Pasquale & Hüsler, Jürg, 2009. "An urn approach to generalized extreme shock models," Statistics & Probability Letters, Elsevier, vol. 79(7), pages 969-976, April.
    6. A. Svensson & J. Holst & R. Lindquist & G. Lindgren, 1996. "Optimal Prediction Of Catastrophes In Autoregressive Moving‐Average Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 17(5), pages 511-531, September.
    7. Muliere, Pietro & Secchi, Piercesare & G. Walker, Stephen, 2003. "Reinforced random processes in continuous time," Stochastic Processes and their Applications, Elsevier, vol. 104(1), pages 117-130, March.
    8. M. Antunes & M. A. Amaral Turkman & K. F. Turkman, 2003. "A Bayesian Approach to Event Prediction," Journal of Time Series Analysis, Wiley Blackwell, vol. 24(6), pages 631-646, November.
    9. Muliere, P. & Secchi, P. & Walker, S. G., 2000. "Urn schemes and reinforced random walks," Stochastic Processes and their Applications, Elsevier, vol. 88(1), pages 59-78, July.
    10. Halfdan Grage & Jan Holst & Georg Lindgren & Mietek Saklak, 2010. "Level Crossing Prediction with Neural Networks," Methodology and Computing in Applied Probability, Springer, vol. 12(4), pages 623-645, December.
    11. Emanuele Amerio & Pietro Muliere & Piercesare Secchi, 2004. "Reinforced Urn Processes For Modeling Credit Default Distributions," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 7(04), pages 407-423.
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    Cited by:

    1. Souto Arias, Luis A. & Cirillo, Pasquale, 2021. "Joint and survivor annuity valuation with a bivariate reinforced urn process," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 174-189.
    2. Stefano Peluso & Antonietta Mira & Pietro Muliere & Alessandro Lomi, 2016. "International Trade: a Reinforced Urn Network Model," Papers 1601.03067, arXiv.org.
    3. Peluso, Stefano & Mira, Antonietta & Muliere, Pietro, 2015. "Reinforced urn processes for credit risk models," Journal of Econometrics, Elsevier, vol. 184(1), pages 1-12.

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