KIV regression converges in strong L2 norm and attains minimax optimal rates over smoothness classes, with improved rates from general spectral regularization in stage one.
The truncation filter function gξ( x) = x−11[x ⩾ξ]yields kernel principal component regression, corresponding to a hard th resholding of eigenvalues at a truncation level ξ
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Nonparametric Instrumental Regression via Kernel Methods is Minimax Optimal
KIV regression converges in strong L2 norm and attains minimax optimal rates over smoothness classes, with improved rates from general spectral regularization in stage one.