Academic Editor: A. Thavaneswaran
Copyright © 2011 Radhey S. Singh and Narinder Kumar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
We extend the results of Gupta and Liang (1998), derived for location parameters, to obtain lower confidence bounds for the probability of correctly selecting the best populations simultaneously for all for the general scale parameter models, where is the number of populations involved in the selection problem. The application of the results to the exponential and normal probability models is discussed. The implementation of the simultaneous lower confidence bounds for is illustrated through real-life datasets.