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A class of self-designing clinical trials is considered which according to an effective but simple, finite learning algorithm consists of automatically adaptively planned weighted group sequential trials with a decision about rejection of the null-hypothesis at each step, but the full...
Persistent link: https://www.econbiz.de/10010982317
This paper deals with the problem of estimating the location parameter of a two parameter exponential distribution in case of contaminated data. Since in this case the sample minimum is an extremely unreliable estimator, robust alternatives are necessary. We investigate two types of estimators...
Persistent link: https://www.econbiz.de/10010982318
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The advanced maximum-linkage-algorithm (AMLA) is a derivative of the maximum-linkage-algorithm (MLA) given by Zerbst (2001). AMLA produces clusterings which have a good separation between the built classes. To reach the separation, centroids were calculated, which are the basis for...
Persistent link: https://www.econbiz.de/10010982322
Im Mess- und Prüfwesen bilden Ringversuche ein Fundament der Qualitätssicherung. Im Mittelpunkt dieses interlaboratoriellen Ringversuchs steht die Messqualität der teilnehmenden Labore. Zudem bietet der Ringversuch die Möglichkeit eines Erfahrungsaustausches, der in einer Verbesserung der...
Persistent link: https://www.econbiz.de/10010982323
Wie schon vor einem Jahr wurde die aktuelle Konjunkturprognose (Anfang 1999) in das RWI Klassifikationsschema eingeordnet. Danach liegt die wirtschaftliche Entwicklung des Zeitraums 1998-3 bis 2000-4 im Grenzbereich zwischen Aufschwung und oberem Wendepunkt. Vor einem Jahr war die damalige...
Persistent link: https://www.econbiz.de/10010982324
We consider the triangle test with replications, i.e. each assessor is asked repeatedly. A commonly used test statistic for this situation is the sum of all correct assessments, summed over all assessors. Several authors (e.g. o’Mahony, 1982, Brockhoff and Schlich, 1998) argue that the...
Persistent link: https://www.econbiz.de/10010982325