@book{KonakovLaeuterLiero1995,
  author    = {Konakov, Valentin S. and L{\"a}uter, Henning and Liero, Hannelore},
  title     = {Nonparametric versus parametric goodness of fit},
  series = {Discussion Paper / Humboldt-Universit{\"a}t zu Berlin, Institut f{\"u}r Mathematik, SFB 373},
  volume    = {49},
  journal   = {Discussion Paper / Humboldt-Universit{\"a}t zu Berlin, Institut f{\"u}r Mathematik, SFB 373},
  address   = {Berlin},
  year      = {1995},
  language  = {en}
}
@book{KonakovLaeuterLiero1995,
  author    = {Konakov, Valentin S. and L{\"a}uter, Henning and Liero, Hannelore},
  title     = {Comparison of the asymptotic power of tests based on L2- and L-norms under non-standard local alternatives},
  series = {Discussion Paper / Humboldt-Universit{\"a}t zu Berlin, Institut f{\"u}r Mathematik, SFB 373},
  volume    = {10},
  journal   = {Discussion Paper / Humboldt-Universit{\"a}t zu Berlin, Institut f{\"u}r Mathematik, SFB 373},
  address   = {Berlin},
  year      = {1995},
  language  = {en}
}
@article{LieroLaeuterKonakov1998,
  author    = {Liero, Hannelore and L{\"a}uter, Henning and Konakov, V. D.},
  title     = {Nonparametric versus parametric goodness of fit},
  issn      = {0323-3944},
  year      = {1998},
  language  = {en}
}
@book{Laeuter2003,
  author    = {L{\"a}uter, Henning},
  title     = {Estimation in partly parametric additive Cox models},
  series = {Preprint / Universit{\"a}t Potsdam, Institut f{\"u}r Mathematik, Arbeitsgruppe Partiell},
  journal   = {Preprint / Universit{\"a}t Potsdam, Institut f{\"u}r Mathematik, Arbeitsgruppe Partiell},
  publisher = {Univ.},
  address   = {Potsdam},
  issn      = {1437-739X},
  pages     = {11 S.},
  year      = {2003},
  language  = {en}
}
@article{Laeuter1996,
  author    = {L{\"a}uter, Henning},
  title     = {Nonparametric versus parametric goodness of fit},
  year      = {1996},
  language  = {en}
}
@article{Laeuter1999,
  author    = {L{\"a}uter, Henning},
  title     = {Nonlinear estimation problems},
  year      = {1999},
  language  = {en}
}
@unpublished{Laeuter2008,
  author    = {L{\"a}uter, Henning},
  title     = {Empirical Minimax Linear Estimates},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-49483},
  year      = {2008},
  abstract  = {We give the explicit solution for the minimax linear estimate. For scale dependent models an empirical minimax linear estimates is de¯ned and we prove that these estimates are Stein's estimates.},
  language  = {en}
}
@book{LaeuterLiero1996,
  author    = {L{\"a}uter, Henning and Liero, Hannelore},
  title     = {Ill-posed inverse problems},
  series = {Discussion Paper / Humboldt-Universit{\"a}t zu Berlin, Institut f{\"u}r Mathematik, SFB 373},
  journal   = {Discussion Paper / Humboldt-Universit{\"a}t zu Berlin, Institut f{\"u}r Mathematik, SFB 373},
  address   = {Berlin},
  year      = {1996},
  language  = {en}
}
@book{LaeuterNikulin1999,
  author    = {L{\"a}uter, Henning and Nikulin, Mikhail S.},
  title     = {Parametric versus nonparametric goodness of fit : another view},
  series = {Discussion paper / Humboldt-Universit{\"a}t zu Berlin, SFB 373, Quantifikation und Simulatio},
  journal   = {Discussion paper / Humboldt-Universit{\"a}t zu Berlin, SFB 373, Quantifikation und Simulatio},
  publisher = {Humboldt-Univ.},
  address   = {Berlin},
  year      = {1999},
  language  = {en}
}
@unpublished{LaeuterRamadan2010,
  author    = {L{\"a}uter, Henning and Ramadan, Ayad},
  title     = {Modeling and Scaling of Categorical Data},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-49572},
  year      = {2010},
  abstract  = {Estimation and testing of distributions in metric spaces are well known. R.A. Fisher, J. Neyman, W. Cochran and M. Bartlett achieved essential results on the statistical analysis of categorical data. In the last 40 years many other statisticians found important results in this field. Often data sets contain categorical data, e.g. levels of factors or names. There does not exist any ordering or any distance between these categories. At each level there are measured some metric or categorical values. We introduce a new method of scaling based on statistical decisions. For this we define empirical probabilities for the original observations and find a class of distributions in a metric space where these empirical probabilities can be found as approximations for equivalently defined probabilities. With this method we identify probabilities connected with the categorical data and probabilities in metric spaces. Here we get a mapping from the levels of factors or names into points of a metric space. This mapping yields the scale for the categorical data. From the statistical point of view we use multivariate statistical methods, we calculate maximum likelihood estimations and compare different approaches for scaling.},
  language  = {de}
}