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Extreme value analysis within a parametric outlier detection framework

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  • S. Cabras
  • J. Morales

Abstract

Threshold selection is a key aspect in extreme values analysis, especially when the sample size is small. The main idea underpinning this work is that extreme observations are assumed to be outliers of a specified parametric model. We propose a threshold selection method based on outlier detection using a suitable measure of surprise. Copyright © 2006 John Wiley & Sons, Ltd.

Suggested Citation

  • S. Cabras & J. Morales, 2007. "Extreme value analysis within a parametric outlier detection framework," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 23(2), pages 157-164, March.
    • Handle: RePEc:wly:apsmbi:v:23:y:2007:i:2:p:157-164
    DOI: 10.1002/asmb.660
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    Cited by:

    1. Juan Gonzalez & Daniela Rodriguez & Mariela Sued, 2013. "Threshold selection for extremes under a semiparametric model," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 22(4), pages 481-500, November.
    2. Lee, J. & Fan, Y. & Sisson, S.A., 2015. "Bayesian threshold selection for extremal models using measures of surprise," Computational Statistics & Data Analysis, Elsevier, vol. 85(C), pages 84-99.
    3. Ringkjøb, Hans-Kristian & Haugan, Peter M. & Solbrekke, Ida Marie, 2018. "A review of modelling tools for energy and electricity systems with large shares of variable renewables," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 440-459.

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