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: endog: array-like
This is the dependent variable.
exog: array-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
bw: array_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. loo_likelihood
()r_squared
()Returns the R-Squared for the nonparametric regression. sig_test
(var_pos[, nboot, nested_res, pivot])Significance test for the variables in the regression.