Classification into two multivariate normal distributions with different covariance matrices

Anderson, T. W. ; Bahadur, R. R. (1962) Classification into two multivariate normal distributions with different covariance matrices Annals of Mathematical Statistics, 33 (2). pp. 420-431. ISSN 0003-4851

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Official URL: http://www.projecteuclid.org/euclid.aoms/117770456...

Related URL: http://dx.doi.org/10.1214/aoms/1177704568

Abstract

Linear procedures for classifying an observation as coming from one of two multivariate normal distributions are studied in the case that the two distributions differ both in mean vectors and covariance matrices. We find the class of admissible linear procedures, which is the minimal complete class of linear procedures. It is shown how to construct the linear procedure which minimizes one probability of misclassification given the other and how to obtain the minimax linear procedure; Bayes linear procedures are also discussed.

Item Type:Article
Source:Copyright of this article belongs to Institute of Mathematical Statistics.
ID Code:27012
Deposited On:08 Dec 2010 12:51
Last Modified:17 May 2016 10:18

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