2002-04-01
articles
A robust Hotelling test
http://link.springer.com/10.1007/s001840200192
2019-04-10T13:05
en
research_article
https://scigraph.springernature.com/explorer/license/
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 [9] instead of the classical mean and covariance matrix. Recently, a fast algorithm was constructed to compute the MCD [10]. 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.
2002-04
125-138
false
Van Aelst
S.
Electrical and Electronic Engineering
G.
Pison
Willems
G.
Department of Mathematics and Computer Science, University of Antwerp (UIA), Universiteitsplein 1, 2610 Wilrijk, Belgium., BE
University of Antwerp
55
dimensions_id
pub.1040082186
Metrika
0026-1335
1435-926X
Engineering
doi
10.1007/s001840200192
readcube_id
beefd175abdc93e3853c127981b1bc7c3b49095cf62b4e7587fd608d6f378f54
P. J.
Rousseeuw
Springer Nature - SN SciGraph project
1