Influence function and asymptotic efficiency of the affine equivariant rank covariance matrix.

Visuri et al (2001) proposed and illustrated the use of the affine equivariant rank covariance matrix (RCM) in classical multivariate inference problems. The RCM was shown to be asymptotically multinormal but explicit formulas for the limiting variances and covariances were not given yet. In this paper the influence functions and the limiting variances and covariances of the RCM and the corresponding scatter estimate are derived in the multivariate elliptic case. Limiting efficiencies are given in the multivariate normal and t-distribution cases. The estimates based on the RCM are highly efficient in the multinormal case, and for heavy tailed distribution, perform better than those based on the regular covariance matrix.Efficiency;

Error-free milestones in error prone measurements

A predictor variable or dose that is measured with substantial error may possess an error-free milestone, such that it is known with negligible error whether the value of the variable is to the left or right of the milestone. Such a milestone provides a basis for estimating a linear relationship between the true but unknown value of the error-free predictor and an outcome, because the milestone creates a strong and valid instrumental variable. The inferences are nonparametric and robust, and in the simplest cases, they are exact and distribution free. We also consider multiple milestones for a single predictor and milestones for several predictors whose partial slopes are estimated simultaneously. Examples are drawn from the Wisconsin Longitudinal Study, in which a BA degree acts as a milestone for sixteen years of education, and the binary indicator of military service acts as a milestone for years of service.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS233 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

A cautionary note on robust covariance plug-in methods

Many multivariate statistical methods rely heavily on the sample covariance matrix. It is well known though that the sample covariance matrix is highly non-robust. One popular alternative approach for "robustifying" the multivariate method is to simply replace the role of the covariance matrix with some robust scatter matrix. The aim of this paper is to point out that in some situations certain properties of the covariance matrix are needed for the corresponding robust "plug-in" method to be a valid approach, and that not all scatter matrices necessarily possess these important properties. In particular, the following three multivariate methods are discussed in this paper: independent components analysis, observational regression and graphical modeling. For each case, it is shown that using a symmetrized robust scatter matrix in place of the covariance matrix results in a proper robust multivariate method.Comment: 24 pages, 7 figure

Hormone effects on fMRI and cognitive measures of encoding: Importance of hormone preparation

We compared fMRI and cognitive data from nine hormone therapy (HT)-naive women with data from women exposed to either opposed conjugated equine estrogens (CEE) (n = 10) or opposed estradiol (n = 4). Exposure to either form of HT was associated with healthier fMRI response; however, CEE-exposed women exhibited poorer memory performance than either HT-naive or estradiol-exposed subjects. These preliminary findings emphasize the need to characterize differential neural effects of various HTs. ©2006AAN Enterprises, Inc