On the upper bound of the risk in selection of the T best items

Abstract

A fixed sample size procedure for selecting the t best system components 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 parameter distributions. The risk function for the Least Favorable Configuration is derived in an integral form for a large class of distribution functionsThis research was supported by the grant DH02-13 of the Bulgarian National Science Fun