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Insights into the role of dynamical features in protein complex formation: The case of SARS-CoV-2 spike binding with ACE2. [PDF]

Comput Struct Biotechnol J
Grassmann G, Miotto M, Alessandrini F, Bo' L, Ruocco G, Milanetti E, Giansanti A.   +6 more
europepmc   +1 more source

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Visualizing distributions of covariance matrices

Journal of Data Science, Statistics, and Visualisation
Statistical graphics are generally designed for visualizing data, but in this case our primary goal is to understand complex multivariate models that might be used as prior distributions for models with unknown covariance matrices.
T. Tokuda, Ben Goodrich, I. Mechelen, A. Gelman, F. Tuerlinckx   +4 more
semanticscholar   +2 more sources

Sample Covariance Matrices

A First Course in Random Matrix Theory, 2020

semanticscholar   +2 more sources

Dimension from covariance matrices

Chaos: An Interdisciplinary Journal of Nonlinear Science, 2017
We describe a method to estimate embedding dimension from a time series. This method includes an estimate of the probability that the dimension estimate is valid. Such validity estimates are not common in algorithms for calculating the properties of dynamical systems.
T. L. Carroll, J. M. Byers
openaire   +2 more sources

Extremal Covariance Matrices

, 2018
The tail dependence coefficient (TDC) is a natural tool to describe extremal dependence. Estimation of the tail dependence coefficient can be performed via empirical process theory. In case of extremal independence, the limit degenerates and hence one cannot construct a test for extremal independence.
Youssouph Cissokho
semanticscholar   +3 more sources

Estimating complex covariance matrices

Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004., 2005
The problem of estimating complex covariance matrices is considered. The objective is to obtain a well behaving estimator that circumvents the weaknesses of the standard sample covariance and regularized estimators. To this end, we use a variational technique that previously has been successfully applied in the real data case.
L. Svensson, M. Lundberg
openaire   +1 more source

Eigen-Adjusted Covariance Matrices

SSRN Electronic Journal, 2011
The Markowitz mean-variance framework is the foundation of modern portfolio theory. One problem with this approach, however, is how sample covariance matrices tend to underestimate risk. Since the biases of optimized portfolios are closely related to eigenfactor portfolios, we present a methodology for estimating biases in eigenfactor volatilities, and
Jose Menchero, Jun Wang, D.J. Orr
openaire   +1 more source