statsmodels.nonparametric.kernel_regression.KernelCensoredReg

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>)

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:

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

bw: array_like

Either a user-specified bandwidth or the method for bandwidth selection. cv_ls: cross-validaton 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

Attributes

———

bw: array_like

The bandwidth parameters

*Methods*

r-squared : calculates the R-Squared coefficient for the model.

fit : calculates the conditional mean and marginal effects.

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
fit([data_predict])	Returns the marginal effects at the data_predict points.
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.