摘要：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 𝑡 best populations (PCS𝑡) simultaneously for all 𝑡=1,…,𝑘−1 for the general scale parameter models, where 𝑘 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 PCS𝑡 is illustrated through real-life datasets.
