class_docstr: class for trimmed and winsorized one sample statistics
  
  axis is None, i.e. ravelling, is not supported
  
  Parameters
  ----------
  data : array-like
      The data, observations to analyze.
  fraction : float in (0, 0.5)
      The fraction of observations to trim at each tail.
      The number of observations trimmed at each tail is
      ``int(fraction * nobs)``
  is_sorted : boolean
      Indicator if data is already sorted. By default the data is sorted
      along ``axis``.
  axis : int
      The axis of reduce operations. By default axis=0, that is observations
      are along the zero dimension, i.e. rows if 2-dim.
docstr: standard error of trimmed mean
          
docstr_tok: ["standard","error","of","trimmed","mean"]
code: def std_mean_trimmed(self):
      """standard error of trimmed mean"""
      se = np.sqrt(self.var_winsorized / self.nobs_reduced)
      # trimming creates correlation across trimmed observations
      # trimming is based on order statistics of the data
      # wilcox 2012, p.61
      se *= np.sqrt(self.nobs / self.nobs_reduced)
      return se
  
file_path: statsmodels/statsmodels/stats/robust_compare.py
imports: import numbers
  import numpy
label_desc: Use image node_id 7 for calling the TrimmedMean obj's underlying member method code with example usage: obj.std_mean_trimmed() and returns: se