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, *, rng=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

var_typestr

The type of the variables, one character per variable:

• c: Continuous

• u: Unordered (Discrete)

• o: Ordered (Discrete)

reg_typestr

Type of regression estimator

• lc: Local Constant Estimator

• ll: Local Linear Estimator

bwstr or array_like, optional

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, optional

Value at which the dependent variable is censored. Default is 0.

defaultsEstimatorSettings instance, optional

The default values for the efficient bandwidth estimation

rng{int, Generator, RandomState}, optional

A seed to use. If None, will use the global RandomState.

Deprecated since version 0.15.0: In release 0.17.0 or after January 2028, whichever comes sooner, using None will initialize a new numpy.random.default_rng using system entropy.

Attributes:

bwarray_like

The bandwidth parameters

Methods

aic_hurvich(bw[, func])	Computes the AIC Hurvich criteria for the estimation of the bandwidth
cv_loo(bw, func)	The cross-validation function with leave-one-out estimator
fit([data_predict])	Returns the mean and marginal effects at the data_predict points
loo_likelihood()	Returns the leave-one-out likelihood function
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

censored

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 mean and marginal effects at the data_predict points
loo_likelihood()	Returns the leave-one-out likelihood function
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