ranking partitioning indifference zone formulation single-stage procedure least favourable configuration loss functions invariance risk partially ranked data Computer Stochastics Laboratory
Issue Date:
Sep-1996
Publisher:
Institute of Mathematics with Computer Center at the Bulgarian Academy of Sciences
Citation:
Preprint
Series/Report no.:
1996;9
Abstract:
A fixed sample size procedure for selecting the t best populations is considered. The probability requirement is set to be satisfied under the indifference zone formulation. In order to minimize the average losses from misclassification, we use loss function which is sensitive to the number of misclassifications. The upper bound of the corresponding risk is derived for location and scale parameter distributions. The risk function for the Least Favorable Configuration is derived in an integral form for a large class of distribution functions. AMS subject classification: 62F07, 62C99, 62A05.
