statsmodels.nonparametric.kernel_regression.KernelCensoredReg.cv_loo

KernelCensoredReg.cv_loo(bw, func)

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 – The value of the CV function

Return type:

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