statsmodels.nonparametric.kernel_density.EstimatorSettings

class statsmodels.nonparametric.kernel_density.EstimatorSettings(efficient=False, randomize=False, n_res=25, n_sub=50, return_median=True, return_only_bw=False, n_jobs=1)
[source]

Object to specify settings for density estimation or regression.
EstimatorSettings
has several proporties related to how bandwidth estimation for the KDEMultivariate
, KDEMultivariateConditional
, KernelReg
and CensoredKernelReg
classes behaves.
Parameters: 

efficient (bool, optional) – If True, the bandwidth estimation is to be performed efficiently – by taking smaller subsamples 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 subsamples of size n_sub so that all samples are used once. 
n_sub (int, optional) – Size of the subsamples. Default is 50.

n_res (int, optional) – The number of random resamples 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 subsample 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. 
Examples
>>> settings = EstimatorSettings(randomize=True, n_jobs=3)
>>> k_dens = KDEMultivariate(data, var_type, defaults=settings)
Methods