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Activity Number:
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153
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Type:
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Contributed
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Date/Time:
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Monday, August 7, 2006 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Computing
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| Abstract - #307165 |
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Title:
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On the Simultaneous Lower Confidence Bounds for Order Restricted Inference
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Author(s):
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Chu-In C. Lee*+ and Jianan Peng and Lin Liu
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Companies:
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Memorial University of Newfoundland and Acadia University and University of California, San Diego
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Address:
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, St. John's, NF, A1C 5S7, Canada
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Keywords:
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Dunnett's procedure ; Gupta's subset selection ; isotonic regression ; Kuhn-Tucker theorem ; lower confidence bounds
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Abstract:
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In many experiments researchers are interested in comparing several treatment means with a control mean. When there are some treatments significantly better than the control, it is often of interest to evaluate the difference between the best treatment mean and the control mean and to identify the best treatment. We derive simultaneous lower confidence bounds for the aforementioned difference for the case that treatments are at least as effective as the control and for the case that no restriction is placed on the treatment means and the control mean. The evaluation of the simultaneous lower confidence bound for the difference between the best treatment mean and the control mean is a concave programming problem subject to homogeneous linear constraints. Two efficient computation algorithms are proposed. Expected lower confidence bounds of the two procedures are compared with Dunnett's.
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