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In this correspondence, we provide a multiple hypothesis test to detect the number of latent noncircular signals in a complex Gaussian random vector. Our method sequentially tests the results of individual generalized likelihood ratio test (GLRT) statistics with known asymptotic distributions to form the multiple hypothesis detector. Specifically, we are able to set a threshold yielding a precise probability of error. This test can be used to statistically determine if a given complex observation is circular Gaussian, and if not, how many latent signals in the observation are noncircular. Simulations are used to quantify the performance of the detector as compared to a detector based on the minimum description length (MDL) criterion. The utility of the detector is shown by applying it to a beamforming application using independent component analysis (ICA). [ABSTRACT FROM AUTHOR]

Abstract: An extension of the whitening transformation for complex random vectors, called the generalized uncorrelating transformation (GUT), is introduced. GUT is a generalization of the strong-uncorrelating transform [J. Eriksson, V. Koivunen, Complex-valued ICA using 2nd-order statistics, in: Proceedings of the IEEE Workshop on Machine Learning for Signal Processing (MLSP’04), Sao Luis, Brazil, 2004] based upon generalized estimators of the covariance and pseudo-covariance matrix, called the scatter matrix and spatial pseudo-scatter matrix, respectively. Depending on the selected scatter and spatial pseudo-scatter matrix, GUT estimators can have largely different statistical properties. Special emphasis is put on robust GUT estimators. We show that GUT is a separating matrix estimator for complex-valued independent component analysis (ICA) when at most one source random variable possess circularly symmetric distribution and sources do not have identical distribution. In the context of ICA, our approach is computationally attractive as it is based on straightforward matrix computations. Simulations and examples are used to confirm reliable performance of our method. [Copyright &y& Elsevier]

We consider the affine equivariant sign covariance matrix (SCM) introduced by Visuri et al. (J. Statist. Plann. Inference 91 (2000) 557). The population SCM is shown to be proportional to the inverse of the regular covariance matrix. The eigenvectors and standardized eigenvalues of the covariance matrix can thus be derived from the SCM. We also construct an estimate of the covariance and correlation matrix based on the SCM. The influence functions and limiting distributions of the SCM and its eigenvectors and eigenvalues are found. Limiting efficiencies are given in multivariate normal and t-distribution cases. The estimates are highly efficient in the multivariate normal case and perform better than estimates based on the sample covariance matrix for heavy-tailed distributions. Simulations confirmed these findings for finite-sample efficiencies. [Copyright &y& Elsevier]

We introduce a new equivariant estimation method of the parameters of the multivariate regression model with q responses and p regressors. The estimate matrix is derived from the lift rank covariance matrix (LRCM) where the lift rank vectors are based on the Oja criterion function. The k = p + q variate ranks and k + 1 variate lift ranks are constructed using hyperplanes (or fits) going through k observations. The new LRCM regression estimate and the least squares (LS) estimate are shown to be weighted sums of the elemental estimates based on these hyperplanes. The LRCM regression estimate is equivariant and convergent, has a limiting multinormal distribution, and is highly efficient in the multivariate normal case. For heavy-tailed distributions, it performs better than the standard LS estimate. Estimation of the variance-covariance matrix of the LRCM estimate is briefly discussed. The theory is illustrated by simulations and a real data example. [ABSTRACT FROM AUTHOR]