NONPARAMETRIC CONFIDENCE REGIONS FOR SOME MULTIVARIATE LOCATION PROBLEMS.
NEW YORK UNIV N Y COURANT INST OF MATHEMATICAL SCIENCES
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Nonparametric methods of constructing confidence regions for the location vectors in the multivariate one-sample and two-sample problems are provided. These methods are based on a class of rank order statistics. Specifically, nonparametric confidence regions based on Bonferroni inequality, the maximum modulus, and Scheffes method are studied. The results obtained are nonparametric generalizations of some of the results of Dunn and Sidak. Certain optimality properties of the proposed methods are also established. Author
- Statistics and Probability