Some Measures for Discriminating between Normal Multivariate Distributions with Equal Covariance Matrices.
STANFORD UNIV CALIF DEPT OF STATISTICS
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Suppose that a statistician is permitted access to data which are more precise under H sub 1 than under H sub 2 where each hypothesis specifies a multivariate normal distribution. He is also allowed a choice between additional data more precise under H sub 1 than under H sub 2 or data in which the reverse is true. In a previous paper it was shown that if a linear discriminant function is used there is a premium on selecting the additional data to be more precise under H sub 1. In the paper this result is extended to the case where the likelihood-ratio test is used. The results involve several alternate measures for discriminating between normal multivariate distributions with unequal covariance matrices. Author
- Statistics and Probability