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The moment of inertia and the linear discriminant function

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  • Reyen, Salem S.
  • Miller, John J.

Abstract

In this note, we show that the characteristic vector of the moment of inertia matrix associated with the first or last characteristic root corresponds to the best linear discriminant function in the situation where the data is a mixture of two multivariate normal distributions with proportional covariance matrices. This result may prove useful as a part of many outlier detection methods. We also describe a small simulation study which illustrates the computational efficiency of the new method.

Suggested Citation

  • Reyen, Salem S. & Miller, John J., 2005. "The moment of inertia and the linear discriminant function," Statistics & Probability Letters, Elsevier, vol. 71(1), pages 39-46, January.
    • Handle: RePEc:eee:stapro:v:71:y:2005:i:1:p:39-46
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    References listed on IDEAS

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    1. Marianthi Markatou, 2000. "Mixture Models, Robustness, and the Weighted Likelihood Methodology," Biometrics, The International Biometric Society, vol. 56(2), pages 483-486, June.
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    Cited by:

    1. Salem Reyen & John Miller & Edward Wegman, 2009. "Separating a mixture of two normals with proportional covariances," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 70(3), pages 297-314, November.

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