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On lower confidence bounds for pcs in truncated location parameter models |
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| Authors: | Shanti S. Gupta Leu Lii - Yuh TaChen Liang |
| Affiliation: | 1. Department of Statistics , Purdue University , 47907, IN;2. Graduate Institute of Statistics , National Central University , Chung-Li, Taiwan, R.O.C;3. Department of Mathematics , Wayne State University , Detroit, 48202, MI |
| Abstract: | We are concerned with deriving lower confidence bounds for the probability of a correct selection in truncated location-parameter models. Two cases are considered according to whether the scale parameter is known or unknown. For each case, a lower confidence bound for the difference between the best and the second best is obtained. These lower confidence bounds are used to construct lower confidence bounds for the probability of a correct selection. The results are then applied to the problem of seleting the best exponential populationhaving the largest truncated location-parameter. Useful tables are provided for implementing the proposed methods. |
| Keywords: | correct selection probability of a correct selection indifference zone lower confidence bound best population truncated-location model two-parameter exponential distribution |