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Selection of variables in two-group discriminant analysis by error rate and Akaike’s information criteria. (English) Zbl 0591.62053
The author considers two criteria for selecting the ”best” subset of variables for the linear discriminant function in the case of two p- variate normal populations $$\Pi_ 1$$, $$\Pi_ 2$$ with different means and a common covariance matrix, the means and the matrix being unknown and are to be estimated by random samples of unequal sizes $$N_ 1$$, $$N_ 2.$$
One criterion is based on minimizing G. J. McLachlan’s asymptotic unbiased estimate [Biometrics 36, 501-510 (1980; Zbl 0442.62046)] for the error rate of misclassification $M(j)=\Phi [-2^{-1}D_ j+2^{- 1}(k_ j-1)(N_ 1^{-1}+N_ 2^{-1})/D_ j+\quad \{32(N_ 1+N_ 2-2)\}^{-1}D_ j\{4(4k_ j-1)-D^ 2_ j\}]$ where $$D_ j$$ is the j-subset sample Mahalanobis distance between $$\Pi_ 1$$ and $$\Pi_ 2$$, and $$k_ j$$ is the dimension of this subset.
The other selection criterion is based on a ”no additional information” model minimizing Akaike’s information criterion $A(j)=(N_ 1+N_ 2)\log \{1+(p-k_ j)F(j)/(N_ 1+N_ 2-p-1)\}+2(k_ j-p),$ $where\quad F(j)=\{(N_ 1+N_ 2-p-1)/(p-k_ j)\}(D^ 2-D^ 2_ j)/\{(N_ 1+N_ 2-2)(N_ 1^{-1\quad}+N_ 2^{-1})+D_ j^ 2\},$ D being the p-variate Mahalanobis distance. It is shown that the expected error rate is closely related to the no additional information model. The asymptotic distributions and error rate risks of both criteria are obtained and are shown to be identical for these criteria, so in this sense the two criteria considered are asymptotically equivalent.
Reviewer: V.Yu.Urbakh

##### MSC:
 62H30 Classification and discrimination; cluster analysis (statistical aspects) 62E20 Asymptotic distribution theory in statistics 62F07 Statistical ranking and selection procedures
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##### References:
 [1] Akaike, H, A new look at the statistical model identification, IEEE trans. automat. control., AC-19, 716-723, (1974) · Zbl 0314.62039 [2] Eisenbeis, R.A; Gilbert, G.G; Avery, R.B, Investigating the relative importance of individual variables and variable subsets in discriminant analysis, Comm. statist., 2, 205-219, (1973) · Zbl 0322.62073 [3] Fujikoshi, Y, A criterion for variable selection in multiple discriminant analysis, Hiroshima math. J., 13, 203-214, (1983) · Zbl 0531.62059 [4] Hablema, J.D.F; Hermans, J, Selection of variables in discriminant by F-statistic and error rate, Technometrics, 19, 487-493, (1977) · Zbl 0369.62002 [5] Krishnaiah, P.R, Selection of variables in discriminant analysis, (), 805-820 · Zbl 0506.62047 [6] Lachenbruch, P; Mickey, M, Estimation of error rates in discriminant analysis, Technometrics, 10, 1-11, (1968) [7] Lachenbruch, P.A, () [8] McLachlan, G.J, An asymptotic unbiased technique for estimating the error rates in discriminant analysis, Biometrics, 30, 239-249, (1974) · Zbl 0288.62027 [9] McLachlan, G.J, A criterion for selecting variables for the linear discriminant function, Biometrics, 32, 529-534, (1976) · Zbl 0334.62023 [10] McLachlan, G.J, On the relationship between the F test and the overall error rate for variable selection in two-group discriminant analysis, Biometrics, 36, 501-510, (1980) · Zbl 0442.62046 [11] Okamoto, M, An asymptotic expansion for the distribution of the linear discriminant function, Ann. math. statist., 34, 1286-1301, (1963) · Zbl 0117.37101 [12] Rao, C.R, Inference on discriminant function coefficients, (), 537-602 [13] Rao, C.R, () [14] Shibata, R, Selection of the order of an autoregressive model by Akaike’s information criterion, Biometrika, 63, 117-126, (1976) · Zbl 0358.62048 [15] Spitzer, F, A combinatorial lemma and its application to probability theory, Trans. amer. math. soc., 82, 323-339, (1956) · Zbl 0071.13003
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