A MAXMIN LINEAR TEST OF NORMAL MEANS AND ITS APPLICATION TO LACHIN DATA

For stochastic ordering tests for normal distributions there exist two well known types of tests. One of them is based on the maximum likelihood ratio principle, the other is the most stringent somewhere most powerful test of Schaafsma and Smid (for a comprehensive treatment see Robertson, Wright and Dykstra (1988), for the latter test also Shi and Kudo (1987)). All these tests are in general numerically tedious. Wei, Lachin (1984) and particularly Lachin (1992) formulate a simple and easily computable test. However, it is not known so far for which sort of ordered alternatives his test is optimal. In this paper it is shown that his procedure is a maxmin test for reasonable subalternatives, provided the covariance matrix has nonnegative row sums. If this property is violated then his procedure can be altered in such a manner that the resulting test again is a maxmin test. An example is given where the modified procedure even in the least favourable case leads to a nontrifling increase in power. The fact that Lachins test resp. the modified version are maxmin tests on appropriate subalternatives amounts to the property that they are maxmin tests on subhypotheses which are relevant in practical applications.