Description Usage Arguments Details Value Author(s) See Also
Function conducts leave-one-out optimization to find lambda using a golden search search with caching. This function is called internally by krls
. It would normally not be called by the user directly.
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L |
Non-negative scalar that determines the lower bound of the search window. Default is |
U |
Positive scalar that determines the upper bound of the search window. Default is |
y |
N by 1 matrix of outcomes. |
Eigenobject |
List that contains the eigenvalues and eigenvectors of the kernel matrix K. |
tol |
Positive scalar that determines the tolerance used in the optimization routine used to find lambda. Default is |
noisy |
If |
eigtrunc |
Positive scalar value that determines truncation of eigenvalues for lamnda search window. See |
By default, upper bound is found as follows: Set j to n, decrease by one until the following is longer true: sum(EigenValues / (EigenValues + j)) < 1.
By default, upper bound is found as follows: Get the position, q, of the eigenvalue that is closest to max(Eigenvalue)/1000. Set j to 0, increase in steps of 0.05 until the below is longer true: sum(EigenValues / (EigenValues + j)) > q.
A scalar that contains the lambda that minimizes the sum of squared leave-one-out errors.
Jens Hainmueller (Stanford) and Chad Hazlett (MIT)
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