class_docstr: 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
  
  Attributes
  ----------
  bw : array_like
      The bandwidth parameters
docstr: Provide something sane to print.
code: def __repr__(self):
      """Provide something sane to print."""
      rpr = "KernelCensoredReg instance\n"
      rpr += "Number of variables: k_vars = " + str(self.k_vars) + "\n"
      rpr += "Number of samples:   nobs = " + str(self.nobs) + "\n"
      rpr += "Variable types:      " + self.var_type + "\n"
      rpr += "BW selection method: " + self._bw_method + "\n"
      rpr += "Estimator type: " + self.reg_type + "\n"
      return rpr
  
imports: import copy
  import numpy
  from scipy import optimize
  from scipy.stats.mstats import mquantiles
  from ._kernel_base import GenericKDE, EstimatorSettings, gpke, LeaveOneOut, _get_type_pos, _adjust_shape, _compute_min_std_IQR, kernel_func
label_desc: Use image node_id 3 for calling the KernelCensoredReg obj's underlying member method code with example usage: obj.__repr__() and returns: rpr