statsmodels.nonparametric.kernel_regression.KernelReg.cv_loo
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KernelReg.cv_loo(bw, func)
[source] -
The cross-validation function with leave-one-out estimator.
Parameters: bw: array_like :
Vector of bandwidth values.
func: callable function :
Returns the estimator of g(x). Can be either
_est_loc_constant
(local constant) or_est_loc_linear
(local_linear).Returns: L: float :
The value of the CV function.
Notes
Calculates the cross-validation least-squares function. This function is minimized by compute_bw to calculate the optimal value of
bw
.For details see p.35 in [2]
..math:: CV(h)=n^{-1}sum_{i=1}^{n}(Y_{i}-g_{-i}(X_{i}))^{2}
where is the leave-one-out estimator of g(X) and is the vector of bandwidths
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