The covariance may be computed using the Numpy function np.cov(). For example, we have two sets of data x and y, np.cov(x, y) returns a 2D array where entries [0,1] and [1,0] are the covariances.

This code is not developped for universal usage and needs to be adapted for specific projects. Sometimes, the data that comes out of a data logger is not a nice continous, corrected, processed file.

An account from first principles is given of a number of aspects of analysis of covariance. Six different meanings of analysis of covariance are outlined and the history of this technique is sketched ...

Comparing large covariance matrices has important applications in modern genomics, where scientists are often interested in understanding whether relationships (e.g., dependencies or co-regulations) ...

Download PDF More Formats on IMF eLibrary Order a Print Copy Create Citation This paper proposes a novel shrinkage estimator for high-dimensional covariance matrices by extending the Oracle ...