class statsmodels.nonparametric.kernel_regression.KernelReg(endog, exog, var_type, reg_type='ll', bw='cv_ls', defaults=<statsmodels.nonparametric._kernel_base.EstimatorSettings object>)
[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: |
|
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bw
array_like – The bandwidth parameters.
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. |
© 2009–2012 Statsmodels Developers
© 2006–2008 Scipy Developers
© 2006 Jonathan E. Taylor
Licensed under the 3-clause BSD License.
http://www.statsmodels.org/stable/generated/statsmodels.nonparametric.kernel_regression.KernelReg.html