Summary
Hotelling’s T2 statistic is an important tool for inference about the center of a multivariate normal population. However, hypothesis tests and confidence intervals based on this statistic can be adversely affected by outliers. Therefore, we construct an alternative inference technique based on a statistic which uses the highly robust MCD estimator (Rousseeuw, 1984) instead of the classical mean and covariance matrix. Recently, a fast algorithm was constructed to compute the MCD (Rousseeuw and Van Driessen, 1999). In our test statistic we use the reweighted MCD, which has a higher efficiency. The distribution of this new statistic differs from the classical one. Therefore, the key problem is to find a good approximation for this distribution. Similarly to the classical T2 distribution, we obtain a multiple of a certain F-distribution. A Monte Carlo study shows that this distribution is an accurate approximation of the true distribution. Finally, the power and the robustness of the one-sample test based on our robust T2 are investigated through simulation.
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References
R.W. Butler, P.L. Davies, and M. Juhn. Asymptotics for the minimum covariance determinant estimator. Ann. Statist., 21:1385–1400, 1993.
C. Croux and G. Haesbroeck. Influence function and efficiency of the minimum covariance determinant scatter matrix estimator. The J. of Multivariate Analysis, 71:161–190, 1999.
J. Hardin and D.M. Rocke. The distribution of robust distances. Technical report, Univ. of California at Davis, 2000.
R.A. Johnson and D.W. Wichern. Applied multivariate statistical analysis. Prentice Hall, New Jersey, 4th edition, 1998.
H.P. Lopuhaä. Asymptotics of reweighted estimators of multivariate location and scatter. Ann. Statist., 27:1638–1665, 1999.
H.P. Lopuhaä and P.J. Rousseeuw. Breakdown points of affine equivariant estimators of multivariate location and covariance matrices. Ann. Statist., 19:229–248, 1991.
K.V. Martha, J.T. Kent, and J.M. Bibby. Multivariate analysis. Academic Press, London, 1995.
G. Pison, S. Van Aelst, and G. Willems. Small sample corrections for LTS and MCD. Metrika, 2002. To appear.
P.J. Rousseeuw. Least median of squares regression. J. Am. Statist. Assoc., 79:871–880, 1984.
P.J. Rousseeuw and K. Van Driessen. A fast algorithm for the minimum covariance determinant estimator. Technometrics, 41(3):212–223, 1999.
P.J. Rousseeuw and B.C. Van Zomeren. Unmasking multivariate outliers and leverage points. J. Am. Statist. Assoc., 85:633–651, 1990.
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© 2003 Springer-Verlag Berlin Heidelberg
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Willems, G., Pison, G., Rousseeuw, P.J., Van Aelst, S. (2003). A Robust Hotelling Test. In: Dutter, R., Filzmoser, P., Gather, U., Rousseeuw, P.J. (eds) Developments in Robust Statistics. Physica, Heidelberg. https://doi.org/10.1007/978-3-642-57338-5_36
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DOI: https://doi.org/10.1007/978-3-642-57338-5_36
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-642-63241-9
Online ISBN: 978-3-642-57338-5
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