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 :
exog: list :
var_type: str :
reg_type: {‘lc’, ‘ll’}, optional :
bw: str or array_like, optional :
defaults: EstimatorSettings instance, optional :
Attributes : ——— : bw: array_like :
**Methods** : r-squared : calculates the R-Squared coefficient for the model. fit : calculates the conditional mean and marginal effects. |
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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. |