Abstract:
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In statistical ranking and selection, the subset selection approach allows the researcher to screen a group of populations, selecting a subset which could be studied more intensively later. Such subset selection is useful when a large number of populations are present. However, due to limited resources at a secondary stage, the experimenters might want to put restrictions on the number of populations to be included in the selected subset. Selection with a restriction on the size of the selected subset is called a restricted subset selection. We propose and study a restricted size subset selection procedure for selecting the largest normal mean among k populations with unknown and unequal variances.
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