How To Find A Valid Covariance Matrix
In some applications (e.g. building data models from only partially observed data) one wants to find the “nearest” covariance matrix to a given symmetric matrix (e.g. of observed covariances). In 2002, Higham formalized the notion of nearness using a weighted Frobenius norm and provided a method for computing the nearest covariance matrix.
Read more about this topic: Covariance Matrix
Famous quotes containing the words find, valid and/or matrix:
“No consensus of men can make an error erroneous. We can only find or commit an error, not create it. When we commit an error, we say what was an error already.”
—Josiah Royce (1855–1916)
“Written poetry is worth reading once, and then should be destroyed. Let the dead poets make way for others. Then we might even come to see that it is our veneration for what has already been created, however beautiful and valid it may be, that petrifies us.”
—Antonin Artaud (1896–1948)
“As all historians know, the past is a great darkness, and filled with echoes. Voices may reach us from it; but what they say to us is imbued with the obscurity of the matrix out of which they come; and try as we may, we cannot always decipher them precisely in the clearer light of our day.”
—Margaret Atwood (b. 1939)