Noncentral Wishart Distribution Research Articles

An Extension of the Non-central Wishart Distribution with Integer Shape Vector

An Extension of the Non-central Wishart Distribution with Integer Shape Vector

A generalized order mixture model for tracing connectivity of white matter fascicles complexity in brain from diffusion MRI.

This paper focuses on tracing the connectivity of white matter fascicles in the brain. In particular, a generalized order algorithm based on mixture of non-central Wishart distribution model is proposed for this purpose. The proposed algorithm utilizes the generalization of integer order based approach with the mixture of non-central Wishart distribution model. Pseudo super anomalous behavior of water diffusion inside human brain is the prime motivation of the the present study. We have shown results on multiple synthetic simulations with fibers orientations in two and three directions in each voxel as well as experiments on real data. Synthetic simulations were performed with varying noise levels and diffusion weighting gradient i.e. $b-$values. The proposed model performed outstanding especially for distinguishing closely oriented fibers.

On the measure of anchored Gaussian simplices, with applications to multivariate medians

We consider anchored Gaussian ℓ-simplices in the d-dimensional Euclidean space, that is, simplices with one fixed vertex y∈Rd and the remaining vertices X1,…,Xℓ randomly sampled from the d-variate standard normal distribution. We determine the distribution of the measure of such simplices for any d, any ℓ, and any anchor point y, which is of interest, e.g., when studying the asymptotic behaviour of U-statistics based on such simplex measures. We provide two proofs of the results. The first one is short but is not self-contained as it crucially relies on a technical result for non-central Wishart distributions. The second one is a simple and self-contained proof, that also provides some geometric insight on the results. Quite nicely, variations on this second argument reveal intriguing distributional identities on products of central and non-central chi-square distributions with Beta-distributed non-centrality parameters. We independently establish these distributional identities by making use of Mellin transforms. Beyond the aforementioned use to study the asymptotic behaviour of some U-statistics, our results do find natural applications in the context of robust location estimation, as we illustrate by considering a class of simplex-based multivariate medians that contains the celebrated spatial median and Oja median as special cases. Throughout, our results are confirmed by numerical experiments.

A symmetric matrix-variate normal local approximation for the Wishart distribution and some applications

The noncentral Wishart distribution has become more mainstream in statistics as the prevalence of applications involving sample covariances with underlying multivariate Gaussian populations as dramatically increased since the advent of computers. Multiple sources in the literature deal with local approximations of the noncentral Wishart distribution with respect to its central counterpart. However, no source has yet developed explicit local approximations for the (central) Wishart distribution in terms of a normal analogue, which is important since Gaussian distributions are at the heart of the asymptotic theory for many statistical methods. In this paper, we prove a precise asymptotic expansion for the ratio of the Wishart density to the symmetric matrix-variate normal density with the same mean and covariances. The result is then used to derive an upper bound on the total variation between the corresponding probability measures and to find the pointwise variance of a new density estimator on the space of positive definite matrices with a Wishart asymmetric kernel. For the sake of completeness, we also find expressions for the pointwise bias of our new estimator, the pointwise variance as we move towards the boundary of its support, the mean squared error, the mean integrated squared error away from the boundary, and we prove its asymptotic normality.

A Brief Derivation of Necessary and Sufficient Conditions for a Family of Matrix Quadratic Forms to Have Mutually Independent Non-Central Wishart Distributions

A Brief Derivation of Necessary and Sufficient Conditions for a Family of Matrix Quadratic Forms to Have Mutually Independent Non-Central Wishart Distributions

Q-space trajectory imaging with positivity constraints (QTI+)

Q-space trajectory imaging (QTI) enables the estimation of useful scalar measures indicative of the local tissue structure. This is accomplished by employing generalized gradient waveforms for diffusion sensitization alongside a diffusion tensor distribution (DTD) model. The first two moments of the underlying DTD are made available by acquisitions at low diffusion sensitivity (b-values). Here, we show that three independent conditions have to be fulfilled by the mean and covariance tensors associated with distributions of symmetric positive semidefinite tensors. We introduce an estimation framework utilizing semi-definite programming (SDP) to guarantee that these conditions are met. Applying the framework on simulated signal profiles for diffusion tensors distributed according to non-central Wishart distributions demonstrates the improved noise resilience of QTI+ over the commonly employed estimation methods. Our findings on a human brain data set also reveal pronounced improvements, especially so for acquisition protocols featuring few number of volumes. Our method’s robustness to noise is expected to not only improve the accuracy of the estimates, but also enable a meaningful interpretation of contrast in the derived scalar maps. The technique’s performance on shorter acquisitions could make it feasible in routine clinical practice.

Estimation of a covariance matrix in multivariate skew-normal distribution

This article addresses the problem of estimating a covariance matrix in a multivariate skew-normal distribution relative to two different losses. The estimation problem can be reduced to that of a scale matrix of a noncentral Wishart distribution. The noncentrality parameter matrix, which is a nuisance parameter, brings about non optimality of the best triangular invariant estimators which are minimax under normality. Some improving techniques under normality are proven to remain robust under the multivariate skew-normal distribution.

On Wishart and noncentral Wishart distributions on symmetric cones

Necessary conditions for the existence of noncentral Wishart distributions are given. Our method relies on positivity properties of spherical polynomials on Euclidean Jordan algebras and advances an approach by Peddada and Richards [Ann. Probab. 19 (1991), pp. 868–874] where only a special case (positive semidefinite matrices, rank $1$ noncentrality parameter) is treated. Not only do the shape parameters need to be in the Wallach set—as is the case for Riesz measures—but also the rank of the noncentrality parameter is constrained by the size of the shape parameter. This rank condition has recently been proved with different methods for the special case of symmetric, positive semidefinite matrices (Letac and Massam; Graczyk, Malecki, and Mayerhofer).

Weighted distributions of eigenvalues

In this article, the weighted version of a probability density function is considered as a mapping of the original distribution. Generally, the properties of the distribution of a random matrix and the distributions of its eigenvalues are closely related. Therefore, the weighted versions of the distributions of the eigenvalues of the Wishart distribution are introduced and their properties are discussed. We propose the concept of rotation invariance for the weighted distributions of the eigenvalues of the Wishart and non-central Wishart distributions. We also introduce here, the concept of a “mirror”, meaning, looking at the distribution of a random matrix through the distribution of its eigenvalues. Some graphical representations are given, to visualize the weighted distributions of the eigenvalues for specific cases.

Secrecy Capacity of Artificial Noise Aided Secure Communication in MIMO Rician Channels

Recent research on the physical layer security of wireless systems focuses on artificial noise (AN) aided security. The main metric for analysis of such systems is the secrecy capacity of the system. Most of the AN schemes proposed in recent research are based on a hypothesis that the number of transmit antennas is larger than that of the receive antennas. Under this assumption, the system can utilize all eigen-subchannels, equal to the number of the receive antennas, to send secret messages. The remaining null spaces are used for transmitting AN signals. These AN signals null out at the legitimate receivers and degrade illegitimate receiver’s channels. However, this strategy can significantly impair the secrecy capacity of the system if the number of transmit antennas is constrained or even smaller than the number of receive antennas. Recently, a new strategy has been proposed, where messages are encoded in $s$ (which is a variable) strongest eigen-subchannels based on ordered eigenvalues of Wishart matrices, while AN signals are generated in remaining spaces. This paper extends this strategy to Rician channels using the complex non-central Wishart distribution. A closed form expression for secrecy capacity of such system is computed using majorization theory. Performance of a MIMO communication system in Rician fading environment is simulated and effect of the Rician factor on secrecy capacity is studied. The same approach is then extended to decode and forward relay network. Secrecy capacity of the said network is computed, and the effect of AN is studied using the same methodology.

The Laplace transform [formula omitted] and the existence of non-central Wishart distributions

The problem considered in this paper is to find when the non-central Wishart distribution, defined on the cone Pd¯ of positive semidefinite matrices of order d and with a real-valued shape parameter p, does exist. This can be reduced to the study of the measures m(n,k,d) defined on Pd¯ and with Laplace transform (dets)−n∕2exptr(s−1w), where n is an integer and w=diag(0,…,0,1,…,1) has order d and rank k. Our two main results are the following: we compute m(d−1,d,d) and we show that neither m(d−2,d,d) nor m(d−2,d−1,d) exists. These facts solve the problems of the existence and computation of these non-central Wishart distributions.

A characterization of Wishart processes and Wishart distributions

A characterization of the existence of non-central Wishart distributions (with shape and non-centrality parameter) as well as the existence of solutions to Wishart stochastic differential equations (with initial data and drift parameter) in terms of their exact parameter domains is given. These two families are the natural extensions of the non-central chi-square distributions and the squared Bessel processes to the positive semidefinite matrices.

Schur Complement Based Analysis of MIMO Zero-Forcing for Rician Fading

For multiple-input/multiple-output (MIMO) spatial multiplexing with zero-forcing detection (ZF), signal-to-noise ratio (SNR) analysis for Rician fading involves the cumbersome noncentral-Wishart distribution (NCWD) of the transmit sample-correlation (Gramian) matrix. An approximation with a virtual CWD previously yielded for the ZF SNR an approximate (virtual) Gamma distribution. However, analytical conditions qualifying the accuracy of the SNR-distribution approximation were unknown. Therefore, we have been attempting to exactly characterize ZF SNR for Rician fading. Our previous attempts succeeded only for the sole Rician-fading stream under Rician–Rayleigh fading, by writing the ZF SNR as scalar Schur complement (SC) in the Gramian. Herein, we pursue a more general matrix-SC-based analysis to characterize SNRs when several streams may undergo Rician fading. On one hand, for full-Rician fading, the SC distribution is found to be exactly a CWD if and only if a channel-mean–correlation condition holds. Interestingly, this CWD then coincides with the virtual CWD ensuing from the approximation . Thus, under the condition , the actual and virtual SNR-distributions coincide. On the other hand, for Rician–Rayleigh fading, the matrix-SC distribution is characterized in terms of the determinant of a matrix with elementary-function entries, which also yields a new characterization of the ZF SNR. Average error probability results validate our analysis vs. simulation.

MIMO Zero-Forcing Detection Analysis for Correlated and Estimated Rician Fading

Experimental modeling of wireless fading channels performed by the WINNER II project has been shown to fit a Rician rather than Rayleigh distribution, the latter being assumed in many analytical studies of multiple-input-multiple-output (MIMO) communication systems. Unfortunately, a Rician MIMO channel matrix has a nonzero mean (i.e., specular component) that yields, for the matrix product that determines the MIMO performance, a noncentral Wishart distribution that is difficult to analyze. Previously, the noncentral Wishart distribution has been approximated, based on a first-order-moment fit, by a central Wishart distribution and used to derive average error probability (AEP) expressions for zero-forcing (ZF) detection. We first reveal that this approximation and the MIMO performance evaluation tools derived from it may be reliable only for rank-one specular matrices. We then exploit this approximation to derive an AEP expression for a lesser known, yet optimal, MIMO ZF approach that, unlike the conventional approach, accounts for channel estimation accuracy through the channel statistics. After validating this AEP expression for the rank-one case, it is shown that the ZF performance averaged over realistic (i.e., WINNER II) distributions of the Rician <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> -factor and azimuth spread (AS) can be much worse than that for the average <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> and AS. Finally, through simulations, it is shown that the optimal detection approach can substantially outperform the conventional approach for ZF for full-rank specular matrices, as well as for minimum mean square error detection for both rank-one and full-rank specular matrices.

On the existence of non-central Wishart distributions

This paper deals with the existence issue of non-central Wishart distributions which is a research topic initiated by Wishart (1928), [10] and with important contributions by e.g., Lévy (1937) [7], Gindikin (1975) [4], Shanbhag (1988) [9], Peddada and Richards (1991) [8]. We present a new method involving the theory of affine Markov processes, which reveals joint necessary conditions on the shape and non-centrality parameter. While Eaton’s conjecture concerning the necessary range of the shape parameter is confirmed, we also observe that it is not sufficient anymore that it only belongs to the Gindikin ensemble, as is in the central case.

A power function of statistical tests dependent on elementary symmetric polynomials

Statistical tests not changed under an affine change of coordinate system are considered in the multivariate analysis. In the case of a multivariate linear model and a model using the canonical correlation analysis, these tests are functions of eigenvalues of matrices following a Wishart distribution. In this paper we prove the monotonicity property of test power functions being functions of elementary symmetric polynomials of eigenvalues of a matrix following a noncentral Wishart distribution.

Finite Dimensional Statistical Inference

In this paper, we derive the explicit series expansion of the eigenvalue distribution of various models, namely the case of non-central Wishart distributions, as well as correlated zero mean Wishart distributions. The tools used extend those of the free probability framework, which have been quite successful for high dimensional statistical inference (when the size of the matrices tends to infinity), also known as free deconvolution. This contribution focuses on the finite Gaussian case and proposes algorithmic methods to compute the moments. Cases where asymptotic results fail to apply are also discussed.

Graph presentations for moments of noncentral Wishart distributions and their applications

We provide formulas for the moments of the real and complex noncentral Wishart distributions of general degrees. The obtained formulas for the real and complex cases are described in terms of the undirected and directed graphs, respectively. By considering degenerate cases, we give explicit formulas for the moments of bivariate chi-square distributions and $2\times 2$ Wishart distributions by enumerating the graphs. Noting that the Laguerre polynomials can be considered to be moments of a noncentral chi-square distributions formally, we demonstrate a combinatorial interpretation of the coefficients of the Laguerre polynomials.

On formulas for moments of the Wishart distributions as weighted generating functions of matchings

We consider the real and complex noncentral Wishart distributions. The moments of these distributions are shown to be expressed as weighted generating functions of graphs associated with the Wishart distributions. We give some bijections between sets of graphs related to moments of the real Wishart distribution and the complex noncentral Wishart distribution. By means of the bijections, we see that calculating these moments of a certain class the real Wishart distribution boils down to calculations for the case of complex Wishart distributions. Nous considérons les lois Wishart non-centrale réel et complexe. Les moments sont décrits comme fonctions génératrices de graphes associées avec les lois Wishart. Nous donnons bijections entre ensembles de graphes relatifs aux moments des lois Wishart non-centrale réel et complexe. Au moyen de la bijection, nous voyons que le calcul des moments d'une certaine classe la loi Wishart réel deviennent le calcul de moments de loi Wishart complexes.

The noncentral Wishart as an exponential family, and its moments

While the noncentral Wishart distribution is generally introduced as the distribution of the random symmetric matrix Y 1 ∗ Y 1 + ⋯ + Y n ∗ Y n where Y 1 , … , Y n are independent Gaussian rows in R k with the same covariance, the present paper starts from a slightly more general definition, following the extension of the chi-square distribution to the gamma distribution. We denote by γ ( p , a ; σ ) this general noncentral Wishart distribution: the real number p is called the shape parameter, the positive definite matrix σ of order k is called the shape parameter and the semi-positive definite matrix a of order k is such that the matrix ω = σ a σ is called the noncentrality parameter. This paper considers three problems: the derivation of an explicit formula for the expectation of tr ( X h 1 ) … tr ( X h m ) when X ∼ γ ( p , a , σ ) and h 1 , … , h m are arbitrary symmetric matrices of order k , the estimation of the parameters ( a , σ ) by a method different from that of Alam and Mitra [K. Alam, A. Mitra, On estimated the scale and noncentrality matrices of a Wishart distribution, Sankhyā, Series B 52 (1990) 133–143] and the determination of the set of acceptable p ’s as already done by Gindikin and Shanbag for the ordinary Wishart distribution γ ( p , 0 , σ ) .