# 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). L – The value of the CV function float

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