class_docstr: Maximum Likelihood Estimation of Linear Model with t-distributed errors
  
  This is an example for generic MLE.
  
  Except for defining the negative log-likelihood method, all
  methods and results are generic. Gradients and Hessian
  and all resulting statistics are based on numerical
  differentiation.
code: def _set_start_params(self, start_params=None, use_kurtosis=False):
      if start_params is not None:
          self.start_params = start_params
      else:
          from statsmodels.regression.linear_model import OLS
  
          res_ols = OLS(self.endog, self.exog).fit()
          start_params = 0.1 * np.ones(self.k_params)
          start_params[: self.k_vars] = res_ols.params
  
          if self.fix_df is False:
              if use_kurtosis:
                  kurt = stats.kurtosis(res_ols.resid)
                  df = 6.0 / kurt + 4
              else:
                  df = 5
  
              start_params[-2] = df
              # TODO adjust scale for df
              start_params[-1] = np.sqrt(res_ols.scale)
  
          self.start_params = start_params
  
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 TLinearModel obj's underlying member method code with example usage: obj._set_start_params(start_params, use_kurtosis) without return types