In this paper, we study the effect of channel estimation error on transmitter design in a multiple input multiple output (MIMO) channel. We assume that only knowledge of either the channel correlation or mean is available at the transmitter and find the transmitting strategy. Under covariance feedback, at the transmitter the channel is modelled as a matrix of zero mean circularly symmetric comp...

multivariate control charts such as hotelling`s t^ 2 and x^ 2 are commonly used for monitoring several related quality characteristics. these control charts use correlation structure that exists between quality characteristics in an attempt to improve monitoring. the purpose of this article is to discuss some issues related to the g chart proposed by levinson et al. [9] for detecting shifts in ...

The basic idea of factor analysis is the following. For a given set of manifest variables x1, . . . , xp one wants to find a set of latent variables ξ1, . . . , ξk, fewer in number than the manifest variables, that contain essentially the same information. The latent variables are supposed to account for the dependencies among the manifest variables in the sense that if the latent variables are...

Remote sensing observations often have correlated errors, but the correlations are typically ignored in data assimilation for numerical weather prediction. The assumption of zero correlations is often used with data thinning methods, resulting in a loss of information. As operational centres move towards higher-resolution forecasting, there is a requirement to retain data providing detail on ap...

The variance–covariance matrix is a multi-dimensional array of numbers, containing information about the individual variabilities and pairwise linear dependence set variables. However, itself difficult to represent in concise way, particularly context multivariate autoregressive conditional heteroskedastic models. common practice report plots k(k−1)/2 time-varying covariances, where k number ma...

The covariance matrix plays an important role in statistical inference, yet modeling a covariance matrix is often a difficult task in practice due to its dimensionality and the non-negative definite constraint. In order to model a covariance matrix effectively, it is typically broken down into components based on modeling considerations or mathematical convenience. Decompositions that have rece...

This article shows that when the nonzero coefficients of the population correlation matrix are all greater in absolute value than (C1 log p/n) 1/2 for some constant C1, we can obtain covariance selection consistency by thresholding the sample correlation matrix. Furthermore, the rate (log p/n) is shown to be optimal.

In geophysical and environmental problems, it is common to have multiple variables of interest measured at the same location and time. These multiple variables typically have dependence over space (and/or time). As a consequence, there is a growing interest in developing models for multivariate spatial processes, in particular, the cross-covariance models. On the other hand, many data sets thes...