KernelCensoredReg.cv_loo()

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 is the leave-one-out estimator of g(X) and is the vector of bandwidths