The robust estimation of multivariate location and shape is one of the most challenging problems in statistics and crucial in many application areas. The objective is to find highly efficient, robust, computable and affine equivariant location and covariance matrix estimates. In this paper three different concepts of multivariate sign and rank are considered and their ability to carry informati...

The performance degradation in traditional adaptive beamformers can be attributed to the imprecise knowledge of the array steering vector and inaccurate estimation of the covariance matrix. The inaccurate estimation of the covariance matrix is due to the limited data samples and presence of desired signal components in the training data. The mismatch between the actual and presumed steering vec...

Appearance-based image analysis techniques require fast computation of principal components of high-dimensional image vectors. We introduce a fast incremental principal component analysis (IPCA) algorithm, called candid covariance-free IPCA (CCIPCA), used to compute the principal components of a sequence of samples incrementally without estimating the covariance matrix (so covariance-free). The...

Several theories of auditory hallucinations implicate the involvement of intrusive thoughts and other theories suggest that the interpretation of voices determines the distress associated with them. This study tested the hypotheses that patients who experience auditory hallucinations will experience more intrusive thoughts and be more distressed by them and interpret them as more uncontrollable...

We derive the bias, variance, covariance, and mean square error of the standard lag windowed correlogram estimator both with and without sample mean removal for complex white noise with an arbitrary mean. We find that the arbitrary mean introduces lag dependent covariance between different lags of the correlogram estimates in spite of the lack of covariance in white noise for non-zeros lags. We...

We consider a uni ed description of classi cation rules for nearly singular covariance matrices. When the covariance matrices of the groups or the pooled covariance matrix become nearly singular, bayesian classi cation rules become seriously unstable. Several procedures have been proposed to tackle this problem, e.g. SIMCA, and Regularized Discriminant Analysis. N s and Indahl (1998) discovered...

There is recent evidence from laboratory experiments and analysis of livestock populations that not only the phenotype itself, but also its environmental variance, is under genetic control. Little is known about the relationships between the environmental variance of one trait and mean levels of other traits, however. A genetic covariance between these is expected to lead to nonlinearity betwee...

Investigations on hippocampal and amygdalar volume have revealed inconsistent results in patients with posttraumatic stress disorder (PTSD). Little is known about the structural covariance alterations between the hippocampus and amygdala in PTSD. In this study, we evaluated the alteration in the hippocampal and amygdalar volume and their structural covariance in the coal mine gas explosion rela...

Aims. We develop and validate tools for estimating residual noise covariance in Planck frequency maps, we also quantify signal error effects and compare different techniques to produce low-resolution maps. Methods. We derived analytical estimates of covariance of the residual noise contained in low-resolution maps produced using a number of mapmaking approaches. We tested these analytical predi...