Rousseeuw
P. J.
Electrical and Electronic Engineering
2003-01-01
2019-04-16T09:10
https://link.springer.com/10.1007%2F978-3-642-57338-5_36
A Robust Hotelling Test
chapter
2003
417-431
en
Hotellingâ€™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.
https://scigraph.springernature.com/explorer/license/
chapters
false
Engineering
Rudolf
Dutter
Willems
G.
978-3-642-57338-5
978-3-642-63241-9
Developments in Robust Statistics
Van Aelst
S.
Springer Nature - SN SciGraph project
Ursula
Gather
University of Antwerp
Department of Mathematics and Computer Science, University of Antwerp (UIA), Belgium
Rousseeuw
Peter J.
Filzmoser
Peter
Heidelberg
Physica-Verlag HD
dimensions_id
pub.1042142954
readcube_id
c8bf81e360e76828bff329b03d5145c9922e45715938cbabb29687e39318d5f0
Pison
G.
doi
10.1007/978-3-642-57338-5_36