technicalreports.bib

@techreport{gebetsberger2018,
  title = {Skewed Logistic Distribution for Statistical Temperature Post-processing in Mountainous Areas},
  author = {Gebetsberger, Manuel and Stauffer, Reto and Mayr, Georg J. and Zeileis, Achim},
  year = {2018},
  institution = {Faculty of Economics and Statistics, University of Innsbruck},
  type = {Working Papers},
  abstract = {Non-homogeneous post-processing is often used to improve the predictive
      performance of probabilistic ensemble forecasts. A common quantity to
      develop, test, and demonstrate new methods is the near-surface air
      temperature frequently assumed to follow a Gaussian response
      distribution. However, Gaussian regression models with only few
      covariates are often not able to account for site-specific local
      features leading to strongly skewed residuals. This residual skewness
      remains even if many covariates are incorporated. Therefore, a simple
      refinement of the classical non-homogeneous Gaussian regression model
      is proposed to overcome this problem by assuming a skewed response
      distribution to account for possible skewness. This study shows a
      comprehensive analysis of the performance of non-homogeneous
      post-processing for 2m temperature for three different site types
      comparing Gaussian, logistic, and skewed logistic response
      distributions. Satisfying results for the skewed logistic
      distribution are found, especially for sites located in mountainous
      areas. Moreover, both alternative model assumptions but in particular
      the skewed response distribution, can improve on the classical
      Gaussian assumption with respect to overall performance, sharpness,
      and calibration of the probabilistic predictions.},
  keywords = {Statistical post-processing; Probabilistic temperature forecast; Skewed distribution; Distributional regression},
  url = {https://EconPapers.repec.org/RePEc:inn:wpaper:2018-06},
  journal = {Advances in Statistical Climatology, Meteorology and Oceanography},
  status = {Revise and resubmit}
}
@techreport{schlosser2018,
  author = {Lisa Schlosser and Torsten Hothorn and Reto Stauffer and Achim Zeileis},
  title = {Distributional Regression Forests for Probabilistic Precipitation Forecasting in Complex Terrain},
  institution = {arXiv.org E-Print Archive},
  year = {2018},
  type = {arXiv},
  number = {1804.02921},
  month = {April},
  url = {http://arxiv.org/abs/1804.02921},
  journal = {Annals of Applied Statistics},
  status = {Under review}
}