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
code: def fit_mle(
      self,
      order,
      start_params=None,
      method="nm",
      maxiter=5000,
      tol=1e-08,
      **kwds,
  ):
      nar, nma = order
      if start_params is not None:
          if len(start_params) != nar + nma + 2:
              raise ValueError(
                  "start_param need sum(order) + 2 elements"
              )
      else:
          start_params = np.concatenate(
              (0.05 * np.ones(nar + nma), [5, 1])
          )
  
      res = super().fit_mle(
          order=order,
          start_params=start_params,
          method=method,
          maxiter=maxiter,
          tol=tol,
          **kwds,
      )
  
      return res
  
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 3 for calling the TArma obj's underlying member method code with example usage: obj.fit_mle(order, start_params, method, maxiter, tol) and returns: res