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Analysis of matrix-variate data is becoming increasingly common in the literature, particularly in the field of clustering and classification. It is well-known that real data, including real matrix-variate data, often exhibit high levels of…

Methodology · Statistics 2024-07-30 Abbas Mahdavi , Narayanaswamy Balakrishnan , Ahad Jamalizadeh

We define generalized bivariate polynomials, from which upon specification of initial conditions the bivariate Fibonacci and Lucas polynomials are obtained. Using essentially a matrix approach we derive identities and inequalities that in…

Combinatorics · Mathematics 2007-05-23 Mario Catalani

The distributions of the smallest and largest eigenvalues for the matrix product $Z^\dagger Z$, where $Z$ is an $n \times m$ complex Gaussian matrix with correlations both along rows and down columns, are expressed as $m \times m$…

Mathematical Physics · Physics 2009-11-11 P. J. Forrester

We give a formula for the tail of the distribution of the non-commutative Rademacher series, which generalizes the result that is already available in the commutative case. As a result, we are able to calculate the norm of these series in…

Functional Analysis · Mathematics 2008-02-03 Stephen J. Montgomery-Smith

The eigenvalue distribution is investigated for matrix models related via the localization to Chern-Simons-matter theories. An integral representation of the planar resolvent is used to derive the positions of the branch points of the…

High Energy Physics - Theory · Physics 2015-05-28 Takao Suyama

A generalization of the generalized inverse Weibull distribution so-called transmuted generalized inverse Weibull dis- tribution is proposed and studied. We will use the quadratic rank transmutation map (QRTM) in order to generate a…

Methodology · Statistics 2013-09-16 Faton Merovci , Ibrahim Elbatal , Alaa Ahmed

A new family of distributions indexed by the class of matrix variate contoured elliptically distribution is proposed as an extension of some bimatrix variate distributions. The termed \emph{multimatrix variate distributions} open new…

Statistics Theory · Mathematics 2024-05-07 José A. Díaz-García , Francisco J. Caro-Lopera

We give a general multiplication-convolution identity for the multivariate and bivariate rank generating polynomial of a matroid. The bivariate rank generating polynomial is transformable to and from the Tutte polynomial by simple algebraic…

Combinatorics · Mathematics 2009-09-15 Joseph P. S. Kung

A method to generate new classes of random matrix ensembles is proposed. Random matrices from these ensembles are Lax matrices of classically integrable systems with a certain distribution of momenta and coordinates. The existence of an…

Chaotic Dynamics · Physics 2011-09-26 E. Bogomolny , O. Giraud , C. Schmit

We explore the limiting empirical eigenvalue distributions arising from matrices of the form \[A_{n+1} = \begin{bmatrix} A_n & I\\ I & A_n \end{bmatrix} , \]where $A_0$ is the adjacency matrix of a $k$-regular graph. We find that for…

Discrete Mathematics · Computer Science 2018-07-23 Clark Alexander , Tara Nenninger , Danielle Tucker

We propose a one-parameter family \ $\mathbb{R}_q$ \ of deformations of the reals, which is motivated by the generalized additivity of the Tsallis entropy. We introduce a generalized multiplication which is distributive with respect to the…

Mathematical Physics · Physics 2015-05-27 Nikos Kalogeropoulos

In this paper, we derive a unified method for establishing the distributional convergence of linear eigenvalue statistics (LES) for generalized patterned random matrices. We prove that for an $N \times N$ generalized patterned random matrix…

Probability · Mathematics 2025-03-14 Kiran Kumar A. S. , Shambhu Nath Maurya , Koushik Saha

Bayesian graphical modeling provides an appealing way to obtain uncertainty estimates when inferring network structures, and much recent progress has been made for Gaussian models. These models have been used extensively in applications to…

Methodology · Statistics 2012-07-06 Michael Finegold , Mathias Drton

We introduce a new family of multivariate distributions by taking the component-wise Tukey-h transformation of a random vector following a skew-normal distribution. The proposed distribution is named the skew-normal-Tukey-h distribution and…

Methodology · Statistics 2023-10-19 Sagnik Mondal , Marc G. Genton

Distribution data refers to a data set where each sample is represented as a probability distribution, a subject area receiving burgeoning interest in the field of statistics. Although several studies have developed…

Methodology · Statistics 2024-02-09 Ryo Okano , Masaaki Imaizumi

In this paper we introduce a new family of operator-valued distributions on Euclidian space acting by convolution on differential forms. It provides a natural generalization of the important Riesz distributions acting on functions, where…

Differential Geometry · Mathematics 2017-02-06 Fischmann Matthias , Ørsted Bent

Various generalizations of Cuntz algebras and their relations to symmetry and duality are reviewed. New generalized Cuntz algebras are associated with a subfactor. A characteristic Hilbert space of basic invariants (with respect to the…

funct-an · Mathematics 2008-02-03 K. -H. Rehren

In this survey, we discuss some basic problems concerning random matrices with discrete distributions. Several new results, tools and conjectures will be presented.

Combinatorics · Mathematics 2007-05-23 V. Vu

We propose a flexible family of distributions, generalized $t$-distributions, on the cylinder which is obtained as a conditional distribution of a trivariate $t$ distribution. The new distribution has unimodality or bimodality, symmetry or…

Methodology · Statistics 2015-07-20 Shonosuke Sugasawa , Kunio Shimizu , Shogo Kato

In this paper, we derive the analytical behavior of the limiting spectral distribution of non-central covariance matrices of the "general information-plus-noise" type, as studied in [14]. Through the equation defining its Stieltjes…

Statistics Theory · Mathematics 2023-06-29 Huanchao Zhou , Jiang Hu , Zhidong Bai , Jack W. Silverstein
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