@unpublished{Buerger2014,
  author    = {B{\"u}rger, Gerd},
  title     = {Comment on "Bias correction, quantile mapping, and downscaling: revisiting the inflation issue"},
  series = {Journal of climate},
  volume    = {27},
  journal   = {Journal of climate},
  number    = {4},
  publisher = {American Meteorological Soc.},
  address   = {Boston},
  issn      = {0894-8755},
  doi       = {10.1175/JCLI-D-13-00184.1},
  pages     = {1819 -- 1820},
  year      = {2014},
  abstract  = {In a recent paper, Maraun describes the adverse effects of quantile mapping on downscaling. He argues that when large-scale GCM variables are rescaled directly to small-scale fields or even station data, genuine small-scale covariability is lost and replaced by uniform variability inherited from the larger scales. This leads to a misrepresentation mainly of areal means and long-term trends. This comment acknowledges the former point, although the argument is relatively old, but disagrees with the latter, showing that grid-size long-term trends can be different from local trends. Finally, because it is partly incorrectly addressed, some clarification is added regarding the inflation issue, stressing that neither randomization nor inflation is free of unverified assumptions.},
  language  = {en}
}
@article{vonderMalsburgAngele2017,
  author    = {von der Malsburg, Titus Raban and Angele, Bernhard},
  title     = {False positives and other statistical errors in standard analyses of eye movements in reading},
  series = {Journal of memory and language},
  volume    = {94},
  journal   = {Journal of memory and language},
  publisher = {Elsevier},
  address   = {San Diego},
  issn      = {0749-596X},
  doi       = {10.1016/j.jml.2016.10.003},
  pages     = {119 -- 133},
  year      = {2017},
  abstract  = {In research on eye movements in reading, it is common to analyze a number of canonical dependent measures to study how the effects of a manipulation unfold over time. Although this gives rise to the well-known multiple comparisons problem, i.e. an inflated probability that the null hypothesis is incorrectly rejected (Type I error), it is accepted standard practice not to apply any correction procedures. Instead, there appears to be a widespread belief that corrections are not necessary because the increase in false positives is too small to matter. To our knowledge, no formal argument has ever been presented to justify this assumption. Here, we report a computational investigation of this issue using Monte Carlo simulations. Our results show that, contrary to conventional wisdom, false positives are increased to unacceptable levels when no corrections are applied. Our simulations also show that counter-measures like the Bonferroni correction keep false positives in check while reducing statistical power only moderately. Hence, there is little reason why such corrections should not be made a standard requirement. Further, we discuss three statistical illusions that can arise when statistical power is low, and we show how power can be improved to prevent these illusions. In sum, our work renders a detailed picture of the various types of statistical errors than can occur in studies of reading behavior and we provide concrete guidance about how these errors can be avoided. (C) 2016 Elsevier Inc. All rights reserved.},
  language  = {en}
}
@article{EbertTrauth2015,
  author    = {Ebert, Thomas and Trauth, Martin H.},
  title     = {Semi-automated detection of annual laminae (varves) in lake sediments using a fuzzy logic algorithm},
  series = {Palaeogeography, palaeoclimatology, palaeoecology : an international journal for the geo-sciences},
  volume    = {435},
  journal   = {Palaeogeography, palaeoclimatology, palaeoecology : an international journal for the geo-sciences},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0031-0182},
  doi       = {10.1016/j.palaeo.2015.05.024},
  pages     = {272 -- 282},
  year      = {2015},
  abstract  = {Annual laminae (varves) in lake sediments are typically visually identified, measured and counted, although numerous attempts have been made to automate this process. The reason for the failure of most of these automated algorithms for varve counting is the complexity of the seasonal laminations, typically rich in lateral fades variations and internal heterogeneities. In the manual counting of varves, the investigator acquired and interpreted flexible numbers of complex decision criteria to understand whether a particular simple lamination is a varve or not. Fuzzy systems simulate the flexible decision making process in a computer by introducing a smooth transition between true varve and false varve. In our investigation, we use an adaptive neuro fuzzy inference system (ANFIS) to detect varves on the basis of a digital image of the sediment. The results of the application of the ANFIS to laminated sediments from the Meerfelder Maar (Eifel, Germany) and from a landslide-dammed lake in the Quebrada de Cafayate of Argentina are compared with manual varve counts and possible reasons for the differences are discussed. (C) 2015 Elsevier B.V. All rights reserved.},
  language  = {en}
}