code: def _kr3(y, alpha=5.0, beta=50.0):
      """
      KR3 estimator from Kim & White
  
      Parameters
      ----------
      y : array_like, 1-d
          Data to compute use in the estimator.
      alpha : float, optional
          Lower cut-off for measuring expectation in tail.
      beta :  float, optional
          Lower cut-off for measuring expectation in center.
  
      Returns
      -------
      kr3 : float
          Robust kurtosis estimator based on standardized lower- and upper-tail
          expected values
  
      Notes
      -----
      .. [*] Tae-Hwan Kim and Halbert White, "On more robust estimation of
         skewness and kurtosis," Finance Research Letters, vol. 1, pp. 56-73,
         March 2004.
      """
      perc = (alpha, 100.0 - alpha, beta, 100.0 - beta)
      lower_alpha, upper_alpha, lower_beta, upper_beta = np.percentile(
          y, perc
      )
      l_alpha = np.mean(y[y < lower_alpha])
      u_alpha = np.mean(y[y > upper_alpha])
  
      l_beta = np.mean(y[y < lower_beta])
      u_beta = np.mean(y[y > upper_beta])
  
      return (u_alpha - l_alpha) / (u_beta - l_beta)
  
file_path: statsmodels/statsmodels/stats/stattools.py
imports: from scipy import stats
  import numpy
  from statsmodels.tools.sm_exceptions import ValueWarning
label_desc: Use image node_id 5 for calling a global function with example usage: _kr3(y, alpha, beta) and returns: unknown