statsmodels.nonparametric.kernel_regression.KernelCensoredReg¶

class statsmodels.nonparametric.kernel_regression.KernelCensoredReg(endog, exog, var_type, reg_type, bw='cv_ls', ckertype='gaussian', ukertype='aitchison_aitken_reg', okertype='wangryzin_reg', censor_val=0, defaults=None)[source]¶

Nonparametric censored regression.

Calculates the conditional 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-validation least squares aic: AIC Hurvich Estimator

ckertype : str, optional¶

The kernel used for the continuous variables.

okertype : str, optional¶

The kernel used for the ordered discrete variables.

ukertype : str, optional¶

The kernel used for the unordered discrete variables.

censor_val : float¶

Value at which the dependent variable is censored

defaults : EstimatorSettings instance, optional¶

The default values for the efficient bandwidth estimation

bw¶

The bandwidth parameters

Type:¶

array_like

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.
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.