Reviewed by Andy Smith Fact checked by Vikki Velasquez Covariance is a statistical measure of how two assets move in relation to each other. It provides diversification and reduces the overall ...

Dobriban, Edgar 2015. Efficient computation of limit spectra of sample covariance matrices. Random Matrices: Theory and Applications, Vol. 04, Issue. 04, p. 1550019.

where nused is the number of non-missing observations and np is the number of estimable parameters. The standard error reported for the parameters is the sqrt of the ...

This year is the centennial anniversary of German psychiatrist Hans Berger's invention of electroencephalography (EEG), a way ...

Robert Stelzer, MathSciNet 'This book deals with the analysis of covariance matrices under two different assumptions: large-sample theory and high-dimensional-data theory. While the former approach is ...

This is the GEE equivalent of the inverse of the Fisher information matrix that is often used in generalized linear models as an estimator of the covariance estimate of the maximum likelihood ...

The parametric method, also known as the variance-covariance method, is a risk management technique for calculating the VaR of a portfolio of assets that first identifies the mean, or expected ...

Dillon and Goldstein (1984) provide the following formal definition of principal components analysis (PCA): Principal components analysis transforms the original set of variables into a smaller set of ...

Adaptive control of sensor arrays with the popular maximum SNR or LMS algorithms may well result in slow adaptive weight vector convergence (depending on the eigenvalues of the noise covariance matrix ...

Limnology and Oceanography Vol. 61, No. S1, 2016 Methane fluxes from a small boreal lake ... Methane fluxes from a small boreal lake measured with the eddy covariance method This is the metadata ...