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 ...

The COV= option must be specified to compute an approximate covariance matrix for the parameter estimates under asymptotic theory for least-squares, maximum-likelihood, or Bayesian estimation, with or ...

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

the eigengenes are eigenvectors to the covariance matrix of the samples. So far we have used data for only two genes to illustrate how PCA works, but what happens when thousands of genes are ...

In particular, they used a "covariance matrix" to flesh out patterns within the data using a statistical technique called principal-component analysis (PCA). PCA is a widely used technique to ...