Multiple decision rules for comparing several populations with a fixed known standard

Independent observations are available from k univariate distributions indexed by a real parameter theata. It is desired to select that distribution with the largest parameter value unless this value is smaller than some fixed standard theata sub 0 in which case no distribution is to be selected. Various single-stage procedures for this (k+1)-decision problem are discussed, using indifference zone, decision theoretic, Bayesian, and subset selection approaches.