statsmodels.nonparametric.kernel_regression.KernelReg.cv_loo¶
- KernelReg.cv_loo(bw, func)[source]¶
The cross-validation function with leave-one-out estimator.
- Parameters:
- bwarray_like
Vector of bandwidth values.
- func
callablefunction 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.
- L
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