statsmodels.nonparametric.kernel_regression.KernelReg¶
-
class
statsmodels.nonparametric.kernel_regression.
KernelReg
(endog, exog, var_type, reg_type='ll', bw='cv_ls', defaults=None)[source]¶ Nonparametric kernel regression class.
Calculates the conditional mean
E[y|X]
wherey = g(X) + e
. Note that the “local constant” type of regression provided here is also known as Nadaraya-Watson kernel regression; “local linear” is an extension of that which suffers less from bias issues at the edge of the support.- Parameters
- endogarray_like
This is the dependent variable.
- exogarray_like
The training data for the independent variable(s) Each element in the list is a separate variable
- var_type
str
The type of the variables, one character per variable:
c: continuous
u: unordered (discrete)
o: ordered (discrete)
- reg_type{‘lc’, ‘ll’},
optional
Type of regression estimator. ‘lc’ means local constant and ‘ll’ local Linear estimator. Default is ‘ll’
- bw
str
or array_like,optional
Either a user-specified bandwidth or the method for bandwidth selection. If a string, valid values are ‘cv_ls’ (least-squares cross-validation) and ‘aic’ (AIC Hurvich bandwidth estimation). Default is ‘cv_ls’. User specified bandwidth must have as many entries as the number of variables.
- defaults
EstimatorSettings
instance
,optional
The default values for the efficient bandwidth estimation.
- Attributes
- bwarray_like
The bandwidth parameters.
Methods
aic_hurvich
(bw[, func])Computes the AIC Hurvich criteria for the estimation of the bandwidth.
cv_loo
(bw, func)The cross-validation function with leave-one-out estimator.
fit
([data_predict])Returns the mean and marginal effects at the data_predict points.
Returns the R-Squared for the nonparametric regression.
sig_test
(var_pos[, nboot, nested_res, pivot])Significance test for the variables in the regression.