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 ...
Proceedings of the American Mathematical Society, Vol. 133, No. 10 (Oct., 2005), pp. 2965-2976 (12 pages) We revisit two results of Curto and Fialkow on moment ...
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 ...
Left: Illustration of different geometric mean properties on the manifold of positive semidefinite matrices implemented in this library. Deviation of the geometric means computed using the ...
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 ...
Approximate Matching,Autoregressive Model,Column Vector,Covariance Matrix,Diagonal Matrix,Factor Analysis,Factor Model,Independent Noise,Kullback-Leibler,Model ...
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 ...
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