Abstract
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. In this article we derive 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 lower confidence bound for the difference between the best treatment mean and the control mean is a concave programming problem subject to homogeneous linear inequality constraints. We propose two efficient computation algorithms and discuss the connection between our procedures and Gupta's subset selection procedure. We compare the expected lower confidence bounds of the two procedures with that of Dunnett's procedure. An application to a real-life data is included.
Published Version
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