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)

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:

endoglist with one element which is array_like

This is the dependent variable.

exoglist

The training data for the independent variable(s) Each element in the list is a separate variable

dep_typestr

The type of the dependent variable(s) c: Continuous u: Unordered (Discrete) o: Ordered (Discrete)

reg_typestr

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

ckertypestr, optional

The kernel used for the continuous variables.

okertypestr, optional

The kernel used for the ordered discrete variables.

ukertypestr, optional

The kernel used for the unordered discrete variables.

censor_valfloat

Value at which the dependent variable is censored

defaultsEstimatorSettings instance, optional

The default values for the efficient bandwidth estimation

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.
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.