IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v212y2014i1p139-15410.1007-s10479-013-1363-y.html
   My bibliography  Save this article

Asymptotic analysis of simultaneous damages in spatial Boolean models

Author

Listed:
  • Haijun Li
  • Susan Xu
  • Way Kuo

Abstract

A notion of the positive spatial association is introduced in this paper to analyze spatial dependence of Boolean models with the focus on estimating the long-range spatial dependence. The explicit tail estimates for probabilities of simultaneous damage to two distant spatial regions are obtained using the regular variation method, and the long-range spatial covariance for the Boolean models with heavy-tailed grains is shown to decay at the power-law rate that is smaller than the tail decay rate of grains. Examples and applications to spatial reliability modeling are also discussed. Copyright Springer Science+Business Media New York 2014

Suggested Citation

  • Haijun Li & Susan Xu & Way Kuo, 2014. "Asymptotic analysis of simultaneous damages in spatial Boolean models," Annals of Operations Research, Springer, vol. 212(1), pages 139-154, January.
    • Handle: RePEc:spr:annopr:v:212:y:2014:i:1:p:139-154:10.1007/s10479-013-1363-y
    DOI: 10.1007/s10479-013-1363-y
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10479-013-1363-y
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10479-013-1363-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Vladik Kreinovich & Monchaya Chiangpradit & Wararit Panichkitkosolkul, 2012. "Efficient algorithms for heavy-tail analysis under interval uncertainty," Annals of Operations Research, Springer, vol. 195(1), pages 73-96, May.
    2. Zhu, Li & Li, Haijun, 2012. "Tail distortion risk and its asymptotic analysis," Insurance: Mathematics and Economics, Elsevier, vol. 51(1), pages 115-121.
    3. Stoyan Stoyanov & Borjana Racheva-Iotova & Svetlozar Rachev & Frank Fabozzi, 2010. "Stochastic models for risk estimation in volatile markets: a survey," Annals of Operations Research, Springer, vol. 176(1), pages 293-309, April.
    4. Li, Haijun, 2003. "Association of multivariate phase-type distributions, with applications to shock models," Statistics & Probability Letters, Elsevier, vol. 64(4), pages 381-392, October.
    5. Harry Joe & Haijun Li, 2011. "Tail Risk of Multivariate Regular Variation," Methodology and Computing in Applied Probability, Springer, vol. 13(4), pages 671-693, December.
    6. Hwang, Jung Yoon & Kuo, Way, 2007. "Model-based clustering for integrated circuit yield enhancement," European Journal of Operational Research, Elsevier, vol. 178(1), pages 143-153, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Olufolajimi Oke & Kavi Bhalla & David C. Love & Sauleh Siddiqui, 2018. "Spatial associations in global household bicycle ownership," Annals of Operations Research, Springer, vol. 263(1), pages 529-549, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xun Lu & Kin Lai & Liang Liang, 2014. "Portfolio value-at-risk estimation in energy futures markets with time-varying copula-GARCH model," Annals of Operations Research, Springer, vol. 219(1), pages 333-357, August.
    2. Qi-Ming He & Jiandong Ren, 2016. "Analysis of a Multivariate Claim Process," Methodology and Computing in Applied Probability, Springer, vol. 18(1), pages 257-273, March.
    3. Hansjörg Albrecher & Martin Bladt & Mogens Bladt, 2021. "Multivariate matrix Mittag–Leffler distributions," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 73(2), pages 369-394, April.
    4. Hashorva, Enkelejd & Ling, Chengxiu & Peng, Zuoxiang, 2014. "Second-order tail asymptotics of deflated risks," Insurance: Mathematics and Economics, Elsevier, vol. 56(C), pages 88-101.
    5. Yinping You & Xiaohu Li, 2017. "Most unfavorable deductibles and coverage limits for multiple random risks with Archimedean copulas," Annals of Operations Research, Springer, vol. 259(1), pages 485-501, December.
    6. Michele Leonardo Bianchi & Gian Luca Tassinari, 2018. "Forward-looking portfolio selection with multivariate non-Gaussian models and the Esscher transform," Papers 1805.05584, arXiv.org, revised May 2018.
    7. Li, Haijun & Wu, Peiling, 2013. "Extremal dependence of copulas: A tail density approach," Journal of Multivariate Analysis, Elsevier, vol. 114(C), pages 99-111.
    8. Alexander Vinel & Pavlo A. Krokhmal, 2017. "Certainty equivalent measures of risk," Annals of Operations Research, Springer, vol. 249(1), pages 75-95, February.
    9. Yuan, Tao & Kuo, Way, 2008. "Spatial defect pattern recognition on semiconductor wafers using model-based clustering and Bayesian inference," European Journal of Operational Research, Elsevier, vol. 190(1), pages 228-240, October.
    10. Das Bikramjit & Fasen-Hartmann Vicky, 2019. "Conditional excess risk measures and multivariate regular variation," Statistics & Risk Modeling, De Gruyter, vol. 36(1-4), pages 1-23, December.
    11. Vladik Kreinovich & Monchaya Chiangpradit & Wararit Panichkitkosolkul, 2012. "Efficient algorithms for heavy-tail analysis under interval uncertainty," Annals of Operations Research, Springer, vol. 195(1), pages 73-96, May.
    12. Li, Haijun & Hua, Lei, 2015. "Higher order tail densities of copulas and hidden regular variation," Journal of Multivariate Analysis, Elsevier, vol. 138(C), pages 143-155.
    13. Cui, Lirong & Li, Haijun, 2007. "Analytical method for reliability and MTTF assessment of coherent systems with dependent components," Reliability Engineering and System Safety, Elsevier, vol. 92(3), pages 300-307.
    14. Li, Jinzhu, 2016. "Uniform asymptotics for a multi-dimensional time-dependent risk model with multivariate regularly varying claims and stochastic return," Insurance: Mathematics and Economics, Elsevier, vol. 71(C), pages 195-204.
    15. Jaume Belles-Sampera & Montserrat Guillén & Miguel Santolino, 2015. "What attitudes to risk underlie distortion risk measure choices?," Working Papers 2015-05, Universitat de Barcelona, UB Riskcenter.
    16. Hou, Yanxi & Wang, Xing, 2019. "Nonparametric inference for distortion risk measures on tail regions," Insurance: Mathematics and Economics, Elsevier, vol. 89(C), pages 92-110.
    17. Yuxin Zhao & Shuai Chang & Chang Liu, 2015. "Multifractal theory with its applications in data management," Annals of Operations Research, Springer, vol. 234(1), pages 133-150, November.
    18. Vincenzo Pacelli & Francesca Pampurini & Anna Grazia Quaranta, 2023. "Environmental, Social and Governance investing: Does rating matter?," Business Strategy and the Environment, Wiley Blackwell, vol. 32(1), pages 30-41, January.
    19. Einmahl, John & Krajina, Andrea, 2023. "Empirical Likelihood Based Testing for Multivariate Regular Variation," Other publications TiSEM 261583f5-c571-48c6-8cea-9, Tilburg University, School of Economics and Management.
    20. Asimit, Alexandru V. & Li, Jinzhu, 2016. "Extremes for coherent risk measures," Insurance: Mathematics and Economics, Elsevier, vol. 71(C), pages 332-341.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:annopr:v:212:y:2014:i:1:p:139-154:10.1007/s10479-013-1363-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.