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Adaptive Verfahren, Robustheit Und Ausreisserbehandlung - Ein Vergleich

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Neuere Verfahren der nichtparametrischen Statistik

Part of the book series: Medizinische Informatik und Statistik ((MEDINFO,volume 60))

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Zusammenfassung

„In adaptive statistical inference, we use the sample to help us select the appropriate type of statistical procedure needed for the situation under consideration“. Mit diesen Worten beginnt R. Hogg seinen Artikel über adaptive Methoden in der Encyclopedia of Statistical Sciences. Damit wird eine intuitiv leicht verständliche Methode gemeint, die eine von Hogg propagierte Verbesserung der statistischen Verfahren erbringt. Es wird unsere Aufgabe sein, die „marktschreierischen“ (Tukey) adaptiven Verfahren mit den bekannten nicht-adaptiven statistischen, Methoden zu vergleichen. Als nicht-adaptiv betrachten wir die robusten Methoden und die Ausreisser-Verfahren. Allen drei genannten Methoden ist die Betrachtungsweise gemeinsam, sich gegen starke Abweichungen vom klassischen Normalverteilungsmodell abzusichern. Unter starken Abweichungen verstehen wir Modellverteilungen, die entgegen der Normalverteilung etwa nicht symmetrisch sind oder wesentlich stärkere oder schwächere Tails aufweisen. Wir werden in den folgenden Vergleichen auch parametrische Methoden miteinbeziehen.

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© 1985 Springer-Verlag Berlin Heidelberg

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Hüsler, J. (1985). Adaptive Verfahren, Robustheit Und Ausreisserbehandlung - Ein Vergleich. In: Pflug, G.C. (eds) Neuere Verfahren der nichtparametrischen Statistik. Medizinische Informatik und Statistik, vol 60. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-70641-7_4

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  • DOI: https://doi.org/10.1007/978-3-642-70641-7_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-15702-1

  • Online ISBN: 978-3-642-70641-7

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