class_docstr: Univariate Arma Model with t-distributed errors
  
  This inherit all methods except loglike from tsa.arma_mle.Arma
  
  This uses the standard t-distribution, the implied variance of
  the error is not equal to scale, but ::
  
      error_variance = df/(df-2)*scale**2
  
  Notes
  -----
  This might be replaced by a standardized t-distribution with scale**2
  equal to variance
docstr: Loglikelihood for arma model for each observation, t-distribute
  
  Notes
  -----
  The ancillary parameter is assumed to be the last element of
  the params vector
docstr_tok: ["Loglikelihood","for","arma","model","for","each","observation",",","t-distribute","Notes","--","--","-","The","ancillary","parameter","is","assumed","to","be","the","last","element","of","the","params","vector"]
code: def nloglikeobs(self, params):
      """
      Loglikelihood for arma model for each observation, t-distribute
  
      Notes
      -----
      The ancillary parameter is assumed to be the last element of
      the params vector
      """
  
      errorsest = self.geterrors(params[:-2])
      # sigma2 = np.maximum(params[-1]**2, 1e-6)  #do I need this
      # axis = 0
      # nobs = len(errorsest)
  
      df = params[-2]
      scale = np.abs(params[-1])
      llike = -stats.t._logpdf(errorsest / scale, df) + np_log(scale)
      return llike
  
file_path: statsmodels/statsmodels/miscmodels/tmodel.py
imports: import numpy
  from scipy import special, stats
  from statsmodels.base.model import GenericLikelihoodModel
  from statsmodels.tsa.arma_mle import Arma
label_desc: Use image node_id 2 for calling the TArma obj's underlying member method code with example usage: obj.nloglikeobs(params) and returns: llike