On the Gaussian representation of the Riesz probability distribution on symmetric matrices

AStA Advances in Statistical Analysis - The Riesz probability distribution on symmetric matrices represents an important extension of the Wishart distribution. It is defined by its Laplace...     

Wilks’ factorization of the matrix-variate Dirichlet–Riesz distributions

The Riesz distributions on positive definite symmetric matrices are used to introduce a class of Dirichlet–Riesz distributions. In addition, several distributional properties are stated. Essentially, we show the relationship between the Dirichlet–Riesz distributions of the first kind and the second kind, respectively. We derive Wilks’ factorization of the matrix-variate Dirichlet–Riesz. Further, several results on the product of Riesz and beta–Riesz matrices with a set of Dirichlet–Riesz matrices of the first kind have been derived.     

Mixture of the Riesz Distribution with Respect to a Multivariate Poisson

The aim of this article is to study a statistical model obtained by the mixture of the Riesz probability distribution on symmetric matrices with respect to a multivariate Poisson distribution. We show that this distribution is related to the modified Bessel function of the first kind. We then determine the domain of the means and the variance function of the generated natural exponential family.     

Riesz inverse Gaussian distributions

The notion of generalized power of a positive definite symmetric matrix and a related notion of generalized Bessel function are used to introduce an extension of the class of matrix generalized inverse Gaussian distributions. The new distributions are shown to arise as conditional distributions of Peirce components of Riesz random matrices. Things are explained in the modern framework of symmetric cones and simple Euclidean Jordan algebra.     

Hadamard matrices and spin models

The concept of spin models was introduced by Jones in 1989. Kawagoe, Munemasa and Watatani generalized it by removing the condition of symmetry. Recently, Bannai and Bannai further generalized the concept of spin models which is called 4-weight spin models or generalized generalized spin models. On the other hand, Ivanov and Chuvaeva showed that symmetric amorphous association schemes of class 4 obtained from Hadamard matrices. An infinite family of Hadamard matrices and of complex Hadamard matrices can be constructed by fusing the relations of these amorphous association schemes.We show the necessary and sufficient condition that these Hadamard matrices give generalized spin models of symmetric Hadamard type and of pseudo-Jones type. A special class of Hadamard matrices satisfies this necessary and sufficient condition. Furthermore, Hadamard matrices constructed from amorphous association schemes are also contained in the special class if Hadamard matrices giving these amorphous association schemes are contained in the special class. It means that there exist infinite families of generalized spin models of symmetric Hadamard type and of pseudo-Jones type.     

Multivariate stable exponential families and Tweedie scale

In this paper, we characterize the multivariate stable natural exponential families by a property of homogeneity of the cumulant function of some basis, and by a property of homogeneity of the variance function. We also extend the definition of a Tweedie scale to a finite dimensional space and we give a class of natural exponential families belonging to this scale on the space of symmetric matrices.     

Optimal minimax squared error risk estimation of the mean of a multivariate normal distribution

Minimax squared error risk estimators of the mean of a multivariate normal distribution are characterized which have smallest Bayes risk with respect to a spherically symmetric prior distribution for (i) squared error loss, and (ii) zero-one loss depending on whether or not estimates are consistent with the hypothesis that the mean is null. In (i), the optimal estimators are the usual Bayes estimators for prior distributions with special structure. In (ii), preliminary test estimators are optimal. The results are obtained by applying the theory of minimax-Bayes-compromise decision problems.     

Identification of probability measures via distribution of quotients

Straightforward generalizations of the classical Kotlarski characterization of normality using bivariate Cauchy distribution of quotients of independent r.v.'s are given. The symmetry assumption in Kotlarski's result is omitted. Two larger families of bivariate distributions are considered: symmetric second kind beta and elliptically contoured measures.     

Geodesic Lagrangian Monte Carlo over the space of positive definite matrices: with application to Bayesian spectral density estimation

We present geodesic Lagrangian Monte Carlo, an extension of Hamiltonian Monte Carlo for sampling from posterior distributions defined on general Riemannian manifolds. We apply this new algorithm to Bayesian inference on symmetric or Hermitian positive definite (PD) matrices. To do so, we exploit the Riemannian structure induced by Cartan's canonical metric. The geodesics that correspond to this metric are available in closed-form and – within the context of Lagrangian Monte Carlo – provide a principled way to travel around the space of PD matrices. Our method improves Bayesian inference on such matrices by allowing for a broad range of priors, so we are not limited to conjugate priors only. In the context of spectral density estimation, we use the (non-conjugate) complex reference prior as an example modelling option made available by the algorithm. Results based on simulated and real-world multivariate time series are presented in this context, and future directions are outlined.     

A note on the moments of the Riesz distribution

This paper derives the first two moments of the two versions of the Riesz distribution in the terms of their characteristic functions.     

A note on the generalisation of cochran’s theorem

Khatri (1968) has extended Cochran’s theorem (1934) to matrices which are not necessarily symmetric. An alternative proof of the theorem is furnished here with some generalization with respect to one of Khatri’s conditions.     

Beta-Riesz distributions on symmetric cones

The Riesz distributions on a symmetric cone are used to introduce a class of beta-Riesz distributions. Some fundamental properties of these distributions are established. In particular, we study the effect of a projection on a beta-Riesz distribution and we give some properties of independence. We also calculate the expectation of a beta-Riesz random variable. As a corollary, we give the regression on the mean of a Riesz random variable; that is, we determine the conditional expectation $E(U\mid U+V)$ where $U$ and $V$ are two independent Riesz random variables.     

Nonparametric bootstrap of sample means of positive-definite matrices with an application to diffusion-tensor-imaging data analysis

This paper presents nonparametric two-sample bootstrap tests for means of random symmetric positive-definite (SPD) matrices according to two different metrics: the Frobenius (or Euclidean) metric, inherited from the embedding of the set of SPD metrics in the Euclidean set of symmetric matrices, and the canonical metric, which is defined without an embedding and suggests an intrinsic analysis. A fast algorithm is used to compute the bootstrap intrinsic means in the case of the latter. The methods are illustrated in a simulation study and applied to a two-group comparison of means of diffusion tensors (DTs) obtained from a single voxel of registered DT images of children in a dyslexia study.     

Invariant tests for the equality of k scale parameters under spherical symmetry

We consider tests for scale parameters when the underlying distribution belongs to the class of spherically symmetric laws. A (nx1) random vector x has a spherically symmetric distribution if the distribution of x is identical to the distribution of Px for all (n×n) orthogonal matrices P. Using the principle of invariance we show that the usual normal-theory tests are not only invariant tests but are also exactly robust with respect to this class of spherically symmetric laws.     

A basic lemma and the analysis of block and Kronecker product designs

In this paper the analysis of the class of block designs whose C matrix can be expressed in terms of the Kronecker product of some elementary matrices is considered. The analysis utilizes a basic result concerning the spectral decomposition of the Kronecker product of symmetric matrices in terms of the spectral decomposition of the component matrices involved in the Kronecker product. The property (A) of Kurkjian and Zelen (1963) is generalised and the analysis of generalised property (A) designs is given. It is proved that a design is balanced factorially if and only if it is a generalised property (A) design. A method of analysis of Kronecker product block designs whose component designs are equi-replicate and proper is also suggested.     

Some recurrence relation-based estimators of the parameters of a generalized log-series distribution

Two families of closed form estimators are proposed for estimating the single parameter of the log-series distribution(LSD)and for estimating the two parameters of a generalization of the LSD distribution(GLSD)presented by Tripathi and Gupta(1985). These families are based on the recurrence relations obtained from these distributions, are of closed form, and have very high asymptotic relative effi¬ciencies. Some two-stage procedures are suggested.     

Continuous Time Wishart Process for Stochastic Risk

Risks are usually represented and measured by volatility-covolatility matrices. Wishart processes are models for a dynamic analysis of multivariate risk and describe the evolution of stochastic volatility-covolatility matrices, constrained to be symmetric positive definite. The autoregressive Wishart process (WAR) is the multivariate extension of the Cox, Ingersoll, Ross (CIR) process introduced for scalar stochastic volatility. As a CIR process it allows for closed-form solutions for a number of financial problems, such as term structure of T-bonds and corporate bonds, derivative pricing in a multivariate stochastic volatility model, and the structural model for credit risk. Moreover, the Wishart dynamics are very flexible and are serious competitors for less structural multivariate ARCH models.     

An extension of the von mises distribution

Directional distribution theory is very useful for the estimation of directional spectra needed for the analysis of time series data. A four parameter directional exponential family is discussed. Depending on the values of its parameters this distribution can be unimodal symmetric, bimodal symmetric, unimodal non-symmetric, or bimodal non-symmetric. The moments of this distribution are found, and equations leading to maximum-likelihood estimates of the parameters along with an outline on numerical procedures for solving these equations are given. FORTRAN subroutines implementing these procedures are available from the authors. Finally, some applications of the new directional density are given.     

Transformation of non positive semidefinite correlation matrices

In multivariate statistics, estimation of the covariance or correlation matrix is of crucial importance. Computational and other arguments often lead to the use of coordinate-dependent estimators, yielding matrices that are symmetric but not positive semidefinite. We briefly discuss existing methods, based on shrinking, for transforming such matrices into positive semidefinite matrices, A simple method based on eigenvalues is also considered. Taking into account the geometric structure of correlation matrices, a new method is proposed which uses techniques similar to those of multidimensional scaling.