efficient: bool, optional :
If True, the bandwidth estimation is to be performed
efficiently – by taking smaller sub-samples and estimating
the scaling factor of each subsample. This is useful for large
samples (nobs >> 300) and/or multiple variables (k_vars > 3).
If False (default), all data is used at the same time.
randomize: bool, optional :
If True, the bandwidth estimation is to be performed by
taking n_res random resamples (with replacement) of size n_sub from
the full sample. If set to False (default), the estimation is
performed by slicing the full sample in sub-samples of size n_sub so
that all samples are used once.
n_sub: int, optional :
Size of the sub-samples. Default is 50.
n_res: int, optional :
The number of random re-samples used to estimate the bandwidth.
Only has an effect if randomize == True. Default value is 25.
return_median: bool, optional :
If True (default), the estimator uses the median of all scaling factors
for each sub-sample to estimate the bandwidth of the full sample.
If False, the estimator uses the mean.
return_only_bw: bool, optional :
If True, the estimator is to use the bandwidth and not the
scaling factor. This is not theoretically justified.
Should be used only for experimenting.
n_jobs : int, optional
The number of jobs to use for parallel estimation with
joblib.Parallel. Default is -1, meaning n_cores - 1, with
n_cores the number of available CPU cores.
See the joblib documentation for more details.