AbstractA d-dimensional positive definite correlation matrix R=(ρij) can be parametrized in terms of the correlations ρi,i+1 for i=1,…,d-1, and the partial correlations ρij|i+1,…j-1 for j-i⩾2. These d2 parameters can independently take values in the interval (-1,1). Hence we can generate a random positive definite correlation matrix by choosing independent distributions Fij, 1⩽i<j⩽d, for these d2 parameters. We obtain conditions on the Fij so that the joint density of (ρij) is proportional to a power of det(R) and hence independent of the order of indices defining the sequence of partial correlations. As a special case, we have a simple construction for generating R that is uniform over the space of positive definite correlation matrices. A...
AbstractMethods are proposed for generating random (p+1)×(p+1) Toeplitz correlation matrices that ar...
In simulation we often have to generate correlated random variables by giving a reference intercorre...
AbstractThis paper derives the Jordan block representation of the so-called pattern correlation matr...
The correlational structure of a set of variables is often conveniently described by the pairwise pa...
AbstractWe present a parameterization of the class PD(n) of positive definite n×n matrices using reg...
In this dissertation a systematic approach for evaluating statistical techniques over a broad range ...
In this dissertation a systematic approach for evaluating statistical techniques over a broad range ...
Correlation coefficients among multiple variables are commonly described in the form of matrices. Ap...
Correlation coefficients among multiple variables are commonly described in the form of matrices. Ap...
The simplest way to describe the dependence for a set of financial assets is their correlation matri...
AbstractWe present a parameterization of the class PD(n) of positive definite n×n matrices using reg...
AbstractWe extend and improve two existing methods of generating random correlation matrices, the on...
An algorithm for generating correlated random variables with known marginal distributions and a spec...
I show analytically that the average of $k$ bootstrapped correlation matrices rapidly becomes positi...
I show analytically that the average of $k$ bootstrapped correlation matrices rapidly becomes positi...
AbstractMethods are proposed for generating random (p+1)×(p+1) Toeplitz correlation matrices that ar...
In simulation we often have to generate correlated random variables by giving a reference intercorre...
AbstractThis paper derives the Jordan block representation of the so-called pattern correlation matr...
The correlational structure of a set of variables is often conveniently described by the pairwise pa...
AbstractWe present a parameterization of the class PD(n) of positive definite n×n matrices using reg...
In this dissertation a systematic approach for evaluating statistical techniques over a broad range ...
In this dissertation a systematic approach for evaluating statistical techniques over a broad range ...
Correlation coefficients among multiple variables are commonly described in the form of matrices. Ap...
Correlation coefficients among multiple variables are commonly described in the form of matrices. Ap...
The simplest way to describe the dependence for a set of financial assets is their correlation matri...
AbstractWe present a parameterization of the class PD(n) of positive definite n×n matrices using reg...
AbstractWe extend and improve two existing methods of generating random correlation matrices, the on...
An algorithm for generating correlated random variables with known marginal distributions and a spec...
I show analytically that the average of $k$ bootstrapped correlation matrices rapidly becomes positi...
I show analytically that the average of $k$ bootstrapped correlation matrices rapidly becomes positi...
AbstractMethods are proposed for generating random (p+1)×(p+1) Toeplitz correlation matrices that ar...
In simulation we often have to generate correlated random variables by giving a reference intercorre...
AbstractThis paper derives the Jordan block representation of the so-called pattern correlation matr...