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statsmodels.nonparametric.kernel_regression.KernelCensoredReg.cv_loo

KernelCensoredReg.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 g_{-i}(X_{i}) is the leave-one-out estimator of g(X) and h is the vector of bandwidths

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