The relative power matrix gives organizers a more sophisticated way to think about shifting the balance of power in their ...

White, H. (1980) A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity. Econometrica, 48, 817-838.

We propose a new approach for covariance change point detection applied to graph signals. Specifically, our method draws on the notion of graph stationarity to derive a relevant parameterization of ...

Visual representation and example of pairwise consistency maximization of factor-graph (Pose-graph). Utilizes Networkx, GTSAM, python for optimization. All pose and graph structures are represented in ...

In this paper, we propose two new algorithms for maximum-likelihood estimation (MLE) of high dimensional sparse covariance matrices. Unlike most of the state-of-the-art methods, which either use ...

This article presents a from-scratch C# implementation of the first technique: compute eigenvalues and eigenvectors from the covariance matrix. If you're not familiar with PCA, most of the terminology ...

This paper proposes a novel shrinkage estimator for high-dimensional covariance matrices by extending the Oracle Approximating Shrinkage (OAS) of Chen et al. (2009) to target the diagonal elements of ...

The extracted information can equivalently be summarized in a matrix, or in a graph, where negative-valued structural covariance is set to zero. Regional degree, clustering coefficient (CC), and ...