Abstract: The correlation matrix is a fundamental statistic that used in many fields. For example, GroupLens, a collaborative filtering system, uses the correlation between users for predictive ...

Vol. 21, No. 1, ET 20th Anniversary Colloquium: Automated Inference and the Future of Econometrics (Feb., 2005), pp. 158-170 (13 pages) This paper proposes a new class of heteroskedastic and ...

Approximate Matching,Autoregressive Model,Column Vector,Covariance Matrix,Diagonal Matrix,Factor Analysis,Factor Model,Independent Noise,Kullback-Leibler,Model ...

This bug report demonstrates inconsistent behavior (see Case 1 and Case 2 below) in SciPy's eigenvalue calculations for singular positive semidefinite (PSD) correlation matrices, specifically in cases ...

A new class of large-sample covariance and spectral density matrix estimators is proposed based on the notion of flat-top kernels. The new estimators are shown to be higher-order accurate when ...

Python proof of concept for BCHOL. Solves for x in Ax = b, using the Recursive Schur Linear Quadratic Regulator explained in the paper A Parallell Linear System Solver for Optimal Control by Brian ...

can be solved by solving an equivalent linear complementarity problem when H is positive semidefinite. The approach is outlined in the discussion of the LCP subroutine in Chapter 17, "Language ...

We develop a general framework for finding approximately-optimal preconditioners for solving linear systems. Leveraging this framework we obtain improved runtimes for fundamental preconditioning and ...

The correlation matrix is of vital importance for value-at-risk (VAR) models in the financial industry. Risk managers are often interested in stressing a subset of market factors within large-scale ...