# 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] 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: 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. Returns the R-Squared for the nonparametric regression. sig_test(var_pos[, nboot, nested_res, pivot]) Significance test for the variables in the regression.
 loo_likelihood