I'm reading the paper "The Power of (Non-)Linear Shrinking: A Review and Guide to Covariance Matrix Estimation" by Ledoit and Wolf (2020). When a function that is used to transform the sample eigenvalues to the diagonal entries of a matrix is being discussed on page 19, it is stated that it converges to a "non-stochastic limiting shrinkage" function, as also displayed in the screenshot below. What would such a function mean?

enter image description here

  • 1
    $\begingroup$ I am not voting close your question again because it includes more details than the one you have deleted. However, even if you have made modifications, unless the question itself is new, pls do not delete your original post and then re-post the question. Pls see this guide on what to do in such cases. $\endgroup$
    – Alper
    Oct 4, 2022 at 17:56
  • $\begingroup$ I deleted the original one because the older and the new questions are the same! And sorry, but I'm newbie in this community. $\endgroup$ Oct 5, 2022 at 7:29


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge that you have read and understand our privacy policy and code of conduct.