# statsmodels.nonparametric.kernel_regression.KernelCensoredReg.cv_loo¶

method

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]

$CV(h)=n^{-1}\sum_{i=1}^{n}(Y_{i}-g_{-i}(X_{i}))^{2}$

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