@unpublished{Eckstein2010,
  author    = {Eckstein, Lars},
  title     = {Think local sell global},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-85537},
  pages     = {12},
  year      = {2010},
  language  = {en}
}
@unpublished{Kunze2010,
  author    = {Kunze, Simone},
  title     = {Das Sammelbilderproblem},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-51646},
  year      = {2010},
  abstract  = {Aus dem Inhalt: 1 Einleitung 2 Entwicklung der L{\"o}sungsans{\"a}tze 3 Martingalansatz 4 Markov-Ketten Ansatz 5 Einbettung in Poisson Prozesse 6 Kombinatorische Ans{\"a}tze 7 Zusammenfassung und Ausblick Literaturverzeichnis},
  language  = {de}
}
@unpublished{LaeuterRamadan2010,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}
}
@unpublished{LaeuterRamadan2010,
  author    = {L{\"a}uter, Henning and Ramadan, Ayad},
  title     = {Statistical Scaling of Categorical Data},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-49566},
  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  = {en}
}
@unpublished{Runge2010,
  author    = {Runge, Antonia},
  title     = {Modellierung der Lebensdauer von Systemen},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-51674},
  year      = {2010},
  abstract  = {Aus dem Inhalt: Einleitung und Zusammenfassung 1 Grundlagen der Lebensdaueranalyse 2 Systemzuverl{\"a}ssigkeit 3 Zensierung 4 Sch{\"a}tzen in nichtparametrischen Modellen 5 Sch{\"a}tzen in parametrischen Modellen 6 Konfidenzintervalle f{\"u}r Parametersch{\"a}tzungen 7 Verteilung einer gemischten Population 8 Kurze Einf{\"u}hrung: Lebensdauer und Belastung 9 Ausblick A R-Quellcode B Symbole und Abk{\"u}rzungen},
  language  = {de}
}
@unpublished{Voss2010,
  author    = {Voss, Carola Regine},
  title     = {Harness-Prozesse},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-49651},
  year      = {2010},
  abstract  = {Harness-Prozesse finden in der Forschung immer mehr Anwendung. Vor allem gewinnen Harness-Prozesse in stetiger Zeit an Bedeutung. Grundlegende Literatur zu diesem Thema ist allerdings wenig vorhanden. In der vorliegenden Arbeit wird die vorhandene Grundlagenliteratur zu Harness-Prozessen in diskreter und stetiger Zeit aufgearbeitet und Beweise ausgef{\"u}hrt, die bisher nur skizziert waren. Ziel dessen ist die Existenz einer Zerlegung von Harness-Prozessen {\"u}ber Z beziehungsweise R+ nachzuweisen.},
  language  = {de}
}