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Union-Intersection Test for the Mean Vector When the Covariance Matrix Is Totally Reducible
Bimal Kumar Sinha and H. S. Wieand
Journal of the American Statistical Association
Vol. 74, No. 366 (Jun., 1979), pp. 340-343
Stable URL: http://www.jstor.org/stable/2286332
Page Count: 4
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The union-intersection test is derived for the mean vector of a multinormal population when the covariance matrix is totally reducible. The performance of the test is compared to those of two other tests, the likelihood ratio test and a test discussed in Young (1976), using an information theory approach, by computing the small-sample powers through Monte Carlo techniques and also by computing the Pitman efficiency.
Journal of the American Statistical Association © 1979 American Statistical Association