statsmodels.nonparametric.kernel_regression.KernelReg¶
-
class
statsmodels.nonparametric.kernel_regression.
KernelReg
(endog, exog, var_type, reg_type='ll', bw='cv_ls', ckertype='gaussian', okertype='wangryzin', ukertype='aitchisonaitken', 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. Note that specifying a custom kernel works only with “local linear” kernel regression. For example, a customtricube
kernel yields LOESS regression.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.
ckertype : str, optional
The kernel used for the continuous variables.
okertype : str, optional
The kernel used for the ordered discrete variables.
ukertype : str, optional
The kernel used for the unordered discrete 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. 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.