Likelihood ratio procedures for subset selection and ranking problems

This report deals with procedures for random-size subset selection fromk(> 2) given populations where the distribution of ir^(i = l, ..., k)has a density f^(x;0^). Let ••• -®[k] denote unknown values ofthe parameters, and let ^[i]» ***'ïï[k] denote the corresponding populations.First, we have considered the problem of selection for consider the/sprocedure that selects TT. if sup L(0;x) > c L(0;x), where L(*;x) is the1 e e u . - - - - -itotal likelihood function, where is the region m the parameter space foriA9= (0^, ..., 0^) having 0^ as the largest component, where 9 is the maximum likelihood estimate of 0 , and where c is a given constant with 0 < c < l .With the densities satisfying seme reasonable requirements given in this report,we have shown that for each i, the probability of includingthe selected subset is decreasing in ®[j] f°r j t i anc* increasing inWe have then derived some results on selection for the t(> 1) best populations,thereby generalizing the results for t = 1. For this problem, we haveconsidered a) selection of a set whose elements consist of subsets of thegiven populations having t members, and requiring that the set of the t• » • • •best populations is included with probability at least P , b) selection ofa subset of the populations so as to include all the t best populationswith probability at least P'*, and c) selection of a subset of the populationssuch that TT[j ^ is included with probability at least P*, j=k-t+l,.•., k. In the final section, we have discussed the relation between thetheories of subset selection based on likelihood ratios and statistical inferenceunder order restrictions, and have considered the complete rankingproblem.There are some occurring misspellings in the formulas in the abstract on this webpage. Read the abstract in the full-text document for correct spelling in formulas, see the downloadable file.digitalisering@um