class statsmodels.nonparametric.kernel_regression.KernelCensoredReg(endog, exog, var_type, reg_type, bw='cv_ls', censor_val=0, defaults=<statsmodels.nonparametric._kernel_base.EstimatorSettings object>)
[source]
Nonparametric censored regression.
Calculates the condtional mean E[y|X]
where y = g(X) + e
, where y is left-censored. Left censored variable Y is defined as Y = min {Y', L}
where L
is the value at which Y
is censored and Y'
is the true value of the variable.
Parameters: |
|
---|
bw
array_like – The bandwidth parameters
aic_hurvich (bw[, func]) | Computes the AIC Hurvich criteria for the estimation of the bandwidth. |
censored (censor_val) | |
cv_loo (bw, func) | The cross-validation function with leave-one-out estimator |
fit ([data_predict]) | Returns the 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.KernelCensoredReg.html