nonparametric.kernel_regression.KernelReg()

statsmodels.nonparametric.kernel_regression.KernelReg

class statsmodels.nonparametric.kernel_regression.KernelReg(endog, exog, var_type, reg_type='ll', bw='cv_ls', defaults=) [source]

Nonparametric kernel regression class.

Calculates the conditional mean E[y|X] where y = 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: list with one element which is array_like : This is the dependent variable. exog: list : 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?. defaults: EstimatorSettings instance, optional : The default values for the efficient bandwidth estimation. Attributes : ??? : bw: array_like : The bandwidth parameters. **Methods** : r-squared : calculates the R-Squared coefficient for the model. fit : calculates the conditional mean and marginal effects.

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.