Abstract
I am going to conclude the general topic of classification and discrimination with a consideration of null hypothesis testing. Much of this chapter deals with the multivariate analysis of variance (MANOVA) and related themes. I have mentioned earlier at several points of the text that testing a multivariate hypothesis of centroid (vector, profile) differences is more complex than testing a univariate hypothesis of mean (location) difference. The basic point to remember is that an inferential test that is the most powerful for detecting a difference when centroids are concentrated is not necessarily the most powerful test for detecting a difference when centroids were diffuse. The general strategy is to treat all unknown differences in structure as if they are diffuse.
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© 1988 Springer-Verlag New York Inc.
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Bernstein, I.H., Garbin, C.P., Teng, G.K. (1988). Classification Methods—Part 3. Inferential Considerations in the MANOVA. In: Applied Multivariate Analysis. Springer, New York, NY. https://doi.org/10.1007/978-1-4613-8740-4_10
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DOI: https://doi.org/10.1007/978-1-4613-8740-4_10
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4613-8742-8
Online ISBN: 978-1-4613-8740-4
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