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.
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docstr: approximate pointwise variance for kernel density
  
  not verified
  
  Parameters
  ----------
  density : array_lie
      pdf of the kernel density
  nobs : int
      number of observations used in the KDE estimation
  
  Returns
  -------
  kde_var : ndarray
      estimated variance of the density estimate
  
  Notes
  -----
  This uses the asymptotic normal approximation to the distribution of
  the density estimate.
code: def density_var(self, density, nobs):
      """approximate pointwise variance for kernel density
  
      not verified
  
      Parameters
      ----------
      density : array_lie
          pdf of the kernel density
      nobs : int
          number of observations used in the KDE estimation
  
      Returns
      -------
      kde_var : ndarray
          estimated variance of the density estimate
  
      Notes
      -----
      This uses the asymptotic normal approximation to the distribution of
      the density estimate.
      """
      return np.asarray(density) * self.L2Norm / self.h / nobs
  
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 6 for calling the CustomKernel obj's underlying member method code with example usage: obj.density_var(density, nobs) and returns: unknown