IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v25y2002i3p245-258.html
   My bibliography  Save this article

Statistical Analysis of the Completeness of a Seismic Catalogue

Author

Listed:
  • R. Rotondi
  • E. Garavaglia

Abstract

Among the numerous issues that the study of seismic events presents, theincompleteness of catalogues is certainly one of the most important. It is also one that only the contribution of many and different skills canprovide with a valid solution. In this paper the search for the complete part of a catalogue is expressed in terms of identification of the changepoint in a hierarchical Bayesian model. Stochastic simulation methods, recently presented in the literature, have enabled us to overcome the computational issues that previously made this approach prohibitive. We have applied the method on data, drawn from the Italian NT4.1.1 catalogue,related to some seismogenetic zones of ZS.4 zonation within which we assumespatial incompleteness to be homogeneous. The results obtained are given inthe concluding sections of the paper. Copyright Kluwer Academic Publishers 2002

Suggested Citation

  • R. Rotondi & E. Garavaglia, 2002. "Statistical Analysis of the Completeness of a Seismic Catalogue," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 25(3), pages 245-258, March.
  • Handle: RePEc:spr:nathaz:v:25:y:2002:i:3:p:245-258
    DOI: 10.1023/A:1014855822358
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1023/A:1014855822358
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1023/A:1014855822358?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. Bradley P. Carlin & Alan E. Gelfand & Adrian F. M. Smith, 1992. "Hierarchical Bayesian Analysis of Changepoint Problems," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 41(2), pages 389-405, June.
    Full references (including those not matched with items on IDEAS)

    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. Fitzpatrick, Matthew, 2014. "Geometric ergodicity of the Gibbs sampler for the Poisson change-point model," Statistics & Probability Letters, Elsevier, vol. 91(C), pages 55-61.
    2. Owyang, Michael T. & Piger, Jeremy & Wall, Howard J., 2008. "A state-level analysis of the Great Moderation," Regional Science and Urban Economics, Elsevier, vol. 38(6), pages 578-589, November.
    3. Ruggieri, Eric & Antonellis, Marcus, 2016. "An exact approach to Bayesian sequential change point detection," Computational Statistics & Data Analysis, Elsevier, vol. 97(C), pages 71-86.
    4. Michael W. Robbins & Colin M. Gallagher & Robert B. Lund, 2016. "A General Regression Changepoint Test for Time Series Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 670-683, April.
    5. DAVID E. ALLEN & MICHAEL McALEER & ROBERT J. POWELL & ABHAY K. SINGH, 2018. "Non-Parametric Multiple Change Point Analysis Of The Global Financial Crisis," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 13(02), pages 1-23, June.
    6. Ľluboš Pástor & Robert F. Stambaugh, 2001. "The Equity Premium and Structural Breaks," Journal of Finance, American Finance Association, vol. 56(4), pages 1207-1239, August.
    7. Gordon, Stephen & Bélanger, Gilles, 1996. "Échantillonnage de Gibbs et autres applications économétriques des chaînes markoviennes," L'Actualité Economique, Société Canadienne de Science Economique, vol. 72(1), pages 27-49, mars.
    8. Gary M. Koop & Simon M. Potter, 2004. "Forecasting and Estimating Multiple Change-point Models with an Unknown Number of Change-points," Discussion Papers in Economics 04/31, Division of Economics, School of Business, University of Leicester.
    9. Li Zhaoyuan & Tian Maozai, 2017. "Detecting Change-Point via Saddlepoint Approximations," Journal of Systems Science and Information, De Gruyter, vol. 5(1), pages 48-73, February.
    10. Rosalia Condorelli, 2013. "A Bayesian analysis of suicide data," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(2), pages 1143-1161, February.
    11. Eric F. Lock & Nidhi Kohli & Maitreyee Bose, 2018. "Detecting Multiple Random Changepoints in Bayesian Piecewise Growth Mixture Models," Psychometrika, Springer;The Psychometric Society, vol. 83(3), pages 733-750, September.
    12. M. Hashem Pesaran & Davide Pettenuzzo & Allan Timmermann, 2006. "Forecasting Time Series Subject to Multiple Structural Breaks," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 73(4), pages 1057-1084.
    13. Griffin, J.E. & Steel, M.F.J., 2011. "Stick-breaking autoregressive processes," Journal of Econometrics, Elsevier, vol. 162(2), pages 383-396, June.
    14. Farhana Sadia & Sarah Boyd & Jonathan M Keith, 2018. "Bayesian change-point modeling with segmented ARMA model," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-23, December.
    15. Gary Koop & Simon M. Potter, 2009. "Prior Elicitation In Multiple Change-Point Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 50(3), pages 751-772, August.
    16. Galeano, Pedro, 2007. "The use of cumulative sums for detection of changepoints in the rate parameter of a Poisson Process," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6151-6165, August.
    17. Chiara Lattanzi & Manuele Leonelli, 2019. "A changepoint approach for the identification of financial extreme regimes," Papers 1902.09205, arXiv.org.
    18. John M. Maheu & Stephen Gordon, 2008. "Learning, forecasting and structural breaks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(5), pages 553-583.
    19. Quintana, Fernando A. & Liu, Jun S. & Pino, Guido E. del, 1999. "Monte Carlo EM with importance reweighting and its applications in random effects models," Computational Statistics & Data Analysis, Elsevier, vol. 29(4), pages 429-444, February.
    20. Wang, Liqun & Lee, Chel Hee, 2014. "Discretization-based direct random sample generation," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 1001-1010.

    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:nathaz:v:25:y:2002:i:3:p:245-258. 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.