Subset selection for an epsilon-best population : efficiency results

An almost best or an \epsilon-best population is defined as a population with location parameter on a distance not larger than \epsilon (\geq 0) from the best population (with largest value of the location parameter). For the subset selection tables with the relative efficiency of selecting an \epsilon-best population relative to selecting the best population are given. Results are presented for confidence level P* = 0.50, 0.80, 0.90, 0.95 and 0.99; the number of populations k =2(1)15(5)50(10)100(50)300(100)500(250)2000, and \epsilon = 0.2, 0.5, 1.0, 1.5 and 2.0, where P* is the minimal probability of correct selection

Subset selection for an epsilon-best population : efficiency results

An almost best or an \epsilon-best population is defined as a population with location parameter on a distance not larger than \epsilon (\geq 0) from the best population (with largest value of the location parameter). For the subset selection tables with the relative efficiency of selecting an \epsilon-best population relative to selecting the best population are given. Results are presented for confidence level P* = 0.50, 0.80, 0.90, 0.95 and 0.99; the number of populations k =2(1)15(5)50(10)100(50)300(100)500(250)2000, and \epsilon = 0.2, 0.5, 1.0, 1.5 and 2.0, where P* is the minimal probability of correct selection

Experiments : design, parametric and nonparametric analysis, and selection

Some general remarks for experimental designs are made. The general statistical methodology of analysis for some special designs is considered. Statistical tests for some specific designs under Normality assumption are indicated. Moreover, nonparametric statistical analyses for some special designs are given. The method of determining the number of observations needed in an experiment is considered in the Normal as well as in the nonparametric situation. Finally, the special topic of designing an experiment in order to select the best out of k(\geq 2) treatments is considered

Two-stage selection procedures with attention to screening

Some literature concerning two-stage selection procedures is given. In general, the problem of selecting the Normal population with largest mean from k(\geq 2) Normal populations with a common variance is considered. Special attention is drawn to two-stage procedures with screening for selecting the best. Such a procedure consists of a combination of the subset selection approach and the indifference zone approach. The first stage is used for eliminating bad populations and the second stage is used to indicate the best population from the remaining part