class_docstr: ARMA model with exact loglikelhood for short time series
  
  Inverts (nobs, nobs) matrix, use only for nobs <= 200 or so.
  
  This class is a pattern for small sample GLS-like models. Intended use
  for loglikelihood of initial observations for ARMA.
  
  
  
  TODO:
  This might be missing the error variance. Does it assume error is
     distributed N(0,1)
  Maybe extend to mean handling, or assume it is already removed.
class_docstr_tok: ["ARMA","model","with","exact","loglikelhood","for","short","time","series","Inverts","(","nobs",",","nobs",")","matrix",",","use","only","for","nobs","<","=","200","or","so",".","This","class","is","a","pattern","for","small","sample","GLS-like","models",".","Intended","use","for","loglikelihood","of","initial","observations","for","ARMA",".","TODO",":","This","might","be","missing","the","error","variance",".","Does","it","assume","error","is","distributed","N","(","0,1",")","Maybe","extend","to","mean","handling",",","or","assume","it","is","already","removed","."]
code: def fit_invertible(self, *args, **kwds):
      res = self.fit(*args, **kwds)
      ma = np.r_[[1], res.params[self.nar : self.nar + self.nma]]
      mainv, wasinvertible = invertibleroots(ma)
      if not wasinvertible:
          start_params = res.params.copy()
          start_params[self.nar : self.nar + self.nma] = mainv[1:]
          # need to add args kwds
          res = self.fit(start_params=start_params)
      return res
  
file_path: statsmodels/statsmodels/miscmodels/try_mlecov.py
imports: import numpy
  from scipy import linalg
  from scipy.linalg import toeplitz
  from statsmodels.base.model import GenericLikelihoodModel
  from statsmodels.datasets import sunspots
  from statsmodels.tsa.arima_process import ArmaProcess, arma_acovf, arma_generate_sample
label_desc: Use image node_id 3 for calling the MLEGLS obj's underlying member method code with example usage: obj.fit_invertible() and returns: res