Both chromatic and luminance-modulated stimuli are served by multiple spatial-frequency-tuned channels. This experiment investigated the independence versus interdependence of spatial frequency channels that serve the detection of red-green chromatic versus yellow-black luminance-modulated stimuli at low spatial frequencies. Contrast thresholds for both chromatic and luminance-modulated grating...

Brain structural covariance networks (SCNs) composed of regions with correlated variation are altered in neuropsychiatric disease and change with age. Little is known about the development of SCNs in early childhood, a period of rapid cortical growth. We investigated the development of structural and maturational covariance networks, including default, dorsal attention, primary visual and senso...

It has been shown that the detection performance can be improved by exploiting the covariance matrix of the channel vector in the distributed MIMO radar. In this paper, we firstly introduce a system model in which the target is modeled as the sum of a finite number of independent scatterers without limitation on transmitter-receiver configurations. Using this system model, we make a detailed an...

In multisensor target tracking, each sensor can have its own target state estimate based on the local sensor measurements. Most existing communication networks between local trackers/sensors transmit to a fusion center the local track estimates–sometimes without any estimation error covariances, sometimes with partial covariance information and only rarely with full covariance information. In o...

The computational cost of Gaussian process regression grows cubically with respect to the number of variables due to the inversion of the covariance matrix, which is impractical for data sets with more than a few thousand nodes. Furthermore, Gaussian processes lack the ability to represent conditional independence assertions between variables. We describe iterative proportional scaling for dire...

The Capon-MVDR (Minimum Variance Distortionless Response) method of frequency-wavenumber spectral estimation requires an invertible spatial covariance estimate. Increasingly, however, one must deal with a singular covariance matrix. The ubiquity of inexpensive sensors implies that the dimension of the covariance matrix is ever-increasing, but coherence times have not changed, so the number of v...

Covariance Intersection (CI) is a major advance over the Kalman Filter for estimation, ltering, and data fusion applications. Covariance Intersection is more general in that it permits the fusion of estimates whose degree of correlation is unknown without imposing distribution assumptions such as known error bounds. Past researchers have always assumed that correlation information is necessary ...

This paper proposes to estimate the covariance matrix of stock returns by an optimally weighted average of two existing estimators: the sample covariance matrix and single-index covariance matrix. This method is generally known as shrinkage, and it is standard in decision theory and in empirical Bayesian statistics. Our shrinkage estimator can be seen as a way to account for extra-market covari...

Many similarity measures used for classification involve the inverse of the group covariance matrices. However, the number of observations available in the training set for each group is, in many cases, significantly inferior to the dimension of the feature space, what implies that the sample covariance matrix is singular. A common solution to this problem is to assume the same covariance matri...

The $H_0$ tension and the accompanying $r_d$ are a hot topic in current cosmology. In order to remove degeneracy between Hubble parameter sound horizon scale from Baryon Acoustic Oscillations (BAO) datasets, we redefine likelihood by marginalizing over $H_0 \cdot r_d$ then perform full Bayesian analysis for different models of dark energy (DE). We find that our uncalibrated early or late physic...