class_docstr: Generic 1D Kernel object.
  Can be constructed by selecting a standard named Kernel,
  or providing a lambda expression and domain.
  The domain allows some algorithms to run faster for finite domain kernels.
class_docstr_tok: ["Generic","1D","Kernel","object",".","Can","be","constructed","by","selecting","a","standard","named","Kernel",",","or","providing","a","lambda","expression","and","domain",".","The","domain","allows","some","algorithms","to","run","faster","for","finite","domain","kernels","."]
docstr: approximate pointwise confidence interval for kernel density
  
  The confidence interval is centered at the estimated density and
  ignores the bias of the density estimate.
  
  not verified
  
  Parameters
  ----------
  density : array_lie
      pdf of the kernel density
  nobs : int
      number of observations used in the KDE estimation
  
  Returns
  -------
  conf_int : ndarray
      estimated confidence interval of the density estimate, lower bound
      in first column and upper bound in second column
  
  Notes
  -----
  This uses the asymptotic normal approximation to the distribution of
  the density estimate. The lower bound can be negative for density
  values close to zero.
code: def density_confint(self, density, nobs, alpha=0.05):
      """approximate pointwise confidence interval for kernel density
  
      The confidence interval is centered at the estimated density and
      ignores the bias of the density estimate.
  
      not verified
  
      Parameters
      ----------
      density : array_lie
          pdf of the kernel density
      nobs : int
          number of observations used in the KDE estimation
  
      Returns
      -------
      conf_int : ndarray
          estimated confidence interval of the density estimate, lower bound
          in first column and upper bound in second column
  
      Notes
      -----
      This uses the asymptotic normal approximation to the distribution of
      the density estimate. The lower bound can be negative for density
      values close to zero.
      """
      from scipy import stats
  
      crit = stats.norm.isf(alpha / 2.0)
      density = np.asarray(density)
      half_width = crit * np.sqrt(self.density_var(density, nobs))
      conf_int = np.column_stack(
          (density - half_width, density + half_width)
      )
      return conf_int
  
imports: from statsmodels.compat.python import lzip, lfilter
  import numpy
  import scipy.integrate
  from scipy.special import factorial
  from numpy import exp, multiply, square, divide, subtract, inf
label_desc: Use image node_id 7 for calling the CustomKernel obj's underlying member method code with example usage: obj.density_confint(density, nobs, alpha) and returns: conf_int