We at Studdridge International can solve the problem of error-maximization for our clients by applying a shrinkage transformation to the eigenvalues of the sample covariance matrix. We push upwards the smallest ones, and pull downwards the largest ones, thereby correcting for their systematic biases.
Our key proprietary technology is knowing how much to shrink the sample eigenvalues. This expertise has been developed over many years. Some earlier versions of our algorithms have been published in peer-reviewed scientific research journals. But when businesses require custom-made solutions that take into account specific features of their data sets, and they only want the best, they turn to Studdridge International.