Extreme Value Prediction for Zero-Inflated Data

  • Fan Xin
  • Zubin Abraham
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7301)

Abstract

Depending on the domain, there may be significant ramifications associated with the occurrence of an extreme event (for e.g., the occurrence of a flood from a climatological perspective). However, due to the relative low occurrence rate of extreme events, the accurate prediction of extreme values is a challenging endeavor. When it comes to zero-inflated time series, standard regression methods such as multiple linear regression and generalized linear models, which emphasize estimating the conditional expected value, are not best suited for inferring extreme values. And so is the case when the the conditional distribution of the data does not conform to the parametric distribution assumed by the regression model. This paper presents a coupled classification and regression framework that focuses on reliable prediction of extreme value events in a zero-inflated time series. The framework was evaluated by applying it on a real-world problem of statistical downscaling of precipitation for the purpose of climate impact assessment studies. The results suggest that the proposed framework is capable of detecting the timing and magnitude of extreme precipitation events effectively compared with several baseline methods.

Keywords

Root Mean Square Error General Linear Model Extreme Event Quantile Regression Statistical Downscaling 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Canadian Climate Change Scenarios Network, Environment Canada, http://www.ccsn.ca/
  2. 2.
    Ancelet, S., Etienne, M.-P., Benot, H., Parent, E.: Modelling spatial zero-inflated continuous data with an exponentially compound poisson process. Environmental and Ecological Statistics (April 2009), doi:10.1007/s10651-009-0111-6Google Scholar
  3. 3.
    Kunkel, E.K., Andsager, K., Easterling, D.: Long-Term Trends in Extreme Precipitation Events over the Conterminous United States and Canada. J. Climate, 2515–2527 (1999)Google Scholar
  4. 4.
    Katz, R.: Statistics of extremes in climate change. Climatic Change, 71–76 (2010)Google Scholar
  5. 5.
    Gaetan, C., Grigoletto, M.: A hierarchical model for the analysis of spatial rainfall extremes. Journal of Agricultural, Biological, and Environmental Statistics (2007)Google Scholar
  6. 6.
    Clarke, R.T.: Estimating trends in data from the Weibull and a generalized extreme value distribution. Water Resources Research (2002)Google Scholar
  7. 7.
    Watterson, I.G., Dix, M.R.: Simulated changes due to global warming in daily precipitation means and extremes and their interpretation using the gamma distribution. Journal of Geophysical Research (2003)Google Scholar
  8. 8.
    Booij, M.J.: Extreme daily precipitation in Western Europe with climate change at appropriate spatial scales. International Journal of Climatology (2002)Google Scholar
  9. 9.
    Ghosh, S., Mallick, B.: A hierarchical Bayesian spatio-temporal model for extreme precipitation events. Environmetrics (2010)Google Scholar
  10. 10.
    Dorland, C., Tol, R.S.J., Palutikof, J.P.: Vulnerability of the Netherlands and Northwest Europe to storm damage under climate change. Climatic Change, 513–535 (1999)Google Scholar
  11. 11.
    Cooley, D., Nychka, D., Naveau, P.: Bayesian spatial modeling of extreme proecipitation return levels. Journal of the American Statistical Association, 824–840 (2007)Google Scholar
  12. 12.
    Clarke, R.T.: Estimating trends in data from the Weibull and a generalized extreme value distribution. Water Resources Research (2002)Google Scholar
  13. 13.
    Wilby, R.L.: Statistical downscaling of daily precipitation using daily airflow and seasonal teleconnection. Climate Research 10, 163–178 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Fan Xin
    • 1
  • Zubin Abraham
    • 2
  1. 1.Department of StatisticsMichigan State UniversityUSA
  2. 2.Department of Computer Science & EngineeringMichigan State UniversityUSA

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