Search

In this article, we consider the problem of constructing confidence intervals for a population median when the underlying population is discrete. We describe seven methods of assigning confidence levels to order statistic based confidence intervals, all of which are easy to implement. A simulation study shows that, with discrete populations, it is possible to obtain consistently more accurate confidence levels and shorter intervals compared to the ones reported by the classical method which is implemented in commercial software. More precisely, the best results are obtained by inverting a two-tailed sign test that properly takes into account tied observations. Some real data examples illustrate the use of these confidence intervals. [ABSTRACT FROM AUTHOR]

A common experimental design for the problem of comparing two means from a normal distribution assumes knowledge of the ratio of the population variances. The optimal sampling ratio is proportional to the square root of this quantity. This article demonstrates that a misspecification of the ratio of the population variances can cause a substantial loss in power of the corresponding tests. As a robust alternative, a maximin approach is used to construct designs, which are efficient, whenever the experimenter is able to specify a specific region for the ratio of the population variances. The advantages of the robust designs for inference in the Behrens-Fisher problem are illustrated in a simulation study and an application to the design of experiment for bioassay is presented. [ABSTRACT FROM AUTHOR]

Presents a data set suitable for demonstrating the X2 test with a rejection region in the low tail. Chi-square test; Random sampling.