nonparametric.kernel_regression.KernelCensoredReg()

statsmodels.nonparametric.kernel_regression.KernelCensoredReg

class statsmodels.nonparametric.kernel_regression.KernelCensoredReg(endog, exog, var_type, reg_type, bw='cv_ls', censor_val=0, defaults=) [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:

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.
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.