statsmodels.nonparametric.kernel_regression.KernelCensoredReg¶
-
class
statsmodels.nonparametric.kernel_regression.
KernelCensoredReg
(endog, exog, var_type, reg_type, bw='cv_ls', censor_val=0, defaults=None)[source]¶ Nonparametric censored regression.
Calculates the conditional mean
E[y|X]
wherey = g(X) + e
, where y is left-censored. Left censored variable Y is defined asY = min {Y', L}
whereL
is the value at whichY
is censored andY'
is the true value of the variable.- 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
- dep_type
str
The type of the dependent variable(s) c: Continuous u: Unordered (Discrete) o: Ordered (Discrete)
- reg_type
str
Type of regression estimator lc: Local Constant Estimator ll: Local Linear Estimator
- bwarray_like
Either a user-specified bandwidth or the method for bandwidth selection. cv_ls: cross-validation least squares aic: AIC Hurvich Estimator
- censor_val
float
Value at which the dependent variable is censored
- defaults
EstimatorSettings
instance
,optional
The default values for the efficient bandwidth estimation
- endog
- Attributes
- bwarray_like
The bandwidth parameters
Methods
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.
Returns the R-Squared for the nonparametric regression.
sig_test
(var_pos[, nboot, nested_res, pivot])Significance test for the variables in the regression.