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Plug-in estimation of d-dimensional density minimum volume set of a rare event in a complex system

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  • J Morio
  • R Pastel

Abstract

Various reliability or hedging problems boil down to quantile estimation. However, real-life systems are usually multidimensional and thus often imply multidimensional density minimum volume set estimation which is usually done with Monte Carlo simulations. Increasing safety standards create a need for density minimum volume set estimation with low probability that crude Monte Carlo cannot fulfil. This paper proposes a new importance sampling algorithm that estimates efficiently multidimensional density minimum volume sets for extreme probability. It also presents some numerical results on a simple bidimensional Gaussian case and on a realistic launcher impact safety zone estimation.

Suggested Citation

  • J Morio & R Pastel, 2012. "Plug-in estimation of d-dimensional density minimum volume set of a rare event in a complex system," Journal of Risk and Reliability, , vol. 226(3), pages 337-345, June.
    • Handle: RePEc:sae:risrel:v:226:y:2012:i:3:p:337-345
    DOI: 10.1177/1748006X11426973
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    References listed on IDEAS

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    1. Neddermeyer, Jan C., 2009. "Computationally Efficient Nonparametric Importance Sampling," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 788-802.
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    3. Chiwoo Park & Jianhua Z. Huang & Yu Ding, 2010. "A Computable Plug-In Estimator of Minimum Volume Sets for Novelty Detection," Operations Research, INFORMS, vol. 58(5), pages 1469-1480, October.
    4. Rubinstein, Reuven Y., 1997. "Optimization of computer simulation models with rare events," European Journal of Operational Research, Elsevier, vol. 99(1), pages 89-112, May.
    5. Baíllo, Amparo & Cuesta-Albertos, Juan A. & Cuevas, Antonio, 2001. "Convergence rates in nonparametric estimation of level sets," Statistics & Probability Letters, Elsevier, vol. 53(1), pages 27-35, May.
    6. Baíllo, Amparo, 2003. "Total error in a plug-in estimator of level sets," Statistics & Probability Letters, Elsevier, vol. 65(4), pages 411-417, December.
    7. Morio, Jérôme, 2011. "Non-parametric adaptive importance sampling for the probability estimation of a launcher impact position," Reliability Engineering and System Safety, Elsevier, vol. 96(1), pages 178-183.
    8. Polonik, Wolfgang, 1997. "Minimum volume sets and generalized quantile processes," Stochastic Processes and their Applications, Elsevier, vol. 69(1), pages 1-24, July.
    9. Jan Neddermeyer, 2011. "Non-parametric partial importance sampling for financial derivative pricing," Quantitative Finance, Taylor & Francis Journals, vol. 11(8), pages 1193-1206.
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