Numerically Stable Generation of Correlation Matrices and Their Factors
By Philip I. Davies, Nicholas John Higham. 2000
125 citations recorded by the source corpus.
Subfields: Computational Theory and Mathematics, Numerical Analysis, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/c48a23cb04e03a1b84d24fb5d0bcdb15e0437a8f
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Theory and Mathematics
In Numerical Analysis
In Statistical and Nonlinear Physics
Citations in the corpus
- The normal law under linear restrictions: simulation and estimation via minimax tilting (2016, methodology): “In the subsequent examples we use the method of Davies and Higham (2000) to simulate random test correlation matrices whose eigenvalues are uniformly distributed over the simplex { x : Example III.”
- Facilitating the application of Support Vector Regression by using a universal Pearson VII function based kernel (2006, background): “More useful and also easier to verify are the following two mathematical properties [17,19], which hold when a matrix A is symmetric: 1.”
- A method for generating realistic correlation matrices (2011, background): “Although there 9 has been additional work expanding the ideas associated with generating random cor10 relation matrices [8, 16, 21, 28, 35] and even randomly generating correlation matrices 11 within particular settings [15, 33], to our knowledge there is no literature devoted to 12 the problem of a”