This paper examines frequentist risks of Bayesian estimates of VAR regression coefficient and error covariance matrices under competing loss functions, under a variety of non-informative priors, and in the normal and Student-t models. Simulation results show that for the regression coefficient matrix an asymmetric LINEX estimator does better overall than the posterior mean. For the error covari...

Pairwise evolutionary distances are a model-based summary statistic for a set of molecular sequences. They represent the leaf-to-leaf path lengths of the underlying phylogenetic tree. Estimates of pairwise distances with overlapping paths covary because of shared mutation events. It is desirable to take these covariance structure into account to increase precision in any process that compares o...

Hardin and Rocke investigated the distribution of the robust Mahalanobis squared distance (RSD) computed using the minimum covariance determinant (MCD) estimator. They showed that the distribution of RSDs for outlying observations not part of the MCD subset is well-approximated by an F distribution. They developed a methodology to adjust an asymptotic formula for the degrees of freedom paramete...

The covariances in spatial models are estimated by linear smoothing of products of residuals. In the model no parametric assumptions are made about the mean function or the spatial dependence. Both are assumed to be smooth. Smoothing is based on local polynomials, though any linear smoother is possible to use. Expressions for the mean and the covariance of this estimator are developed and a ver...

The Wilks’ Lambda Statistic (likelihood ratio test, LRT) is a commonly used tool for inference about the mean vectors of several multivariate normal populations. However, it is well known that the Wilks’ Lambda statistic which is based on the classical normal theory estimates of generalized dispersions, is extremely sensitive to the influence of outliers. A robust multivariate statistic for the...

In the context of radar detection, the clutter covariance matrix estimation is an important point to design optimal detectors. While the Gaussian clutter case has been extensively studied, the new advances in radar technology show that non-Gaussian clutter models have to be considered. Among these models, the spherically invariant random vector modelling is particularly interesting since it inc...

Medical imaging datasets often contain deviant observations, the so-called outliers, due to acquisition or preprocessing artifacts or resulting from large intrinsic inter-subject variability. These can undermine the statistical procedures used in group studies as the latter assume that the cohorts are composed of homogeneous samples with anatomical or functional features clustered around a cent...