Robust version of Hotelling $T^2$ test I am looking for a robust version of Hotelling's $T^2$ test for the mean of a vector. As data, I have a $m\ \times\  n$ matrix, $X$, each row an i.i.d. sample of an $n$-dimensional RV, $x$. The null hypothesis I wish to test is $E[x] = \mu$, where $\mu$ is a fixed $n$-dimensional vector. The classical Hotelling test appears to be susceptible to non-normality in the distribution of $x$ (just as the 1-d analogue, the Student t-test is susceptible to skew and kurtosis). 
what is the state of the art robust version of this test? I am looking for something relatively fast and conceptually simple. There was a paper in COMPSTAT 2008 on the topic, but I do not have access to the proceedings. Any help? 
 A: Sure: two answers
a) If by robustness, you mean robust to outliers, then run Hottelling's T-test using a robust estimation of scale/scatter: you will find all the explications and R code here:
http://www.statsravingmad.com/blog/statistics/a-robust-hotelling-test/
b) if by robustness you mean optimal under large group of distributions, then you should go for a sign based T2 (ask if this what you want, by the tone of your question i think not).
PS: this is the paper you want;
Roelant, E., Van Aelst, S., and Willems, G. (2008), “Fast Bootstrap for Robust Hotelling Tests,” COMPSTAT 2008: Proceedings in Computational Statistics (P. Brito, Ed.) Heidelberg: Physika-Verlag, to appear.
A: Some robust alernatives are discussed in A class of robust stepwise alternativese to Hotelling's T 2 tests, which deals with trimmed means of the marginals of residuals produced by stepwise regression, and in A comparison of robust alternatives to Hoteslling's T^2 control chart, which outlines some robust alternatives based on MVE, MCD, RMCD and trimmed means.
