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Extreme values identification in regression using a peaks-over-threshold approach

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  • Tong Siu Tung Wong
  • Wai Keung Li

Abstract

The problem of heavy tail in regression models is studied. It is proposed that regression models are estimated by a standard procedure and a statistical check for heavy tail using residuals is conducted as a tool for regression diagnostic. Using the peaks-over-threshold approach, the generalized Pareto distribution quantifies the degree of heavy tail by the extreme value index. The number of excesses is determined by means of an innovative threshold model which partitions the random sample into extreme values and ordinary values. The overall decision on a significant heavy tail is justified by both a statistical test and a quantile-quantile plot. The usefulness of the approach includes justification of goodness of fit of the estimated regression model and quantification of the occurrence of extremal events. The proposed methodology is supplemented by surface ozone level in the city center of Leeds.

Suggested Citation

  • Tong Siu Tung Wong & Wai Keung Li, 2015. "Extreme values identification in regression using a peaks-over-threshold approach," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(3), pages 566-576, March.
    • Handle: RePEc:taf:japsta:v:42:y:2015:i:3:p:566-576
    DOI: 10.1080/02664763.2014.978843
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    References listed on IDEAS

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    1. Huixia Judy Wang & Deyuan Li & Xuming He, 2012. "Estimation of High Conditional Quantiles for Heavy-Tailed Distributions," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(500), pages 1453-1464, December.
    2. Lee, David & Li, Wai Keung & Wong, Tony Siu Tung, 2012. "Modeling insurance claims via a mixture exponential model combined with peaks-over-threshold approach," Insurance: Mathematics and Economics, Elsevier, vol. 51(3), pages 538-550.
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    5. Emma F. Eastoe & Jonathan A. Tawn, 2009. "Modelling non‐stationary extremes with application to surface level ozone," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(1), pages 25-45, February.
    6. Janet E. Heffernan & Jonathan A. Tawn, 2004. "A conditional approach for multivariate extreme values (with discussion)," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(3), pages 497-546, August.
    7. Wang, Hansheng & Tsai, Chih-Ling, 2009. "Tail Index Regression," Journal of the American Statistical Association, American Statistical Association, vol. 104(487), pages 1233-1240.
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    Cited by:

    1. Tser-Yieth Chen & Chi-Jui Huang, 2019. "Dual Pathways of Value Endorsement in Green Marketing," Sustainability, MDPI, vol. 11(8), pages 1-23, April.

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