class_docstr: Maximum Likelihood Estimation of Linear Model with t-distributed errors
  
  This is an example for generic MLE which has the same
  statistical model as discretemod.Poisson.
  
  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.
class_docstr_tok: ["Maximum","Likelihood","Estimation","of","Linear","Model","with","t-distributed","errors","This","is","an","example","for","generic","MLE","which","has","the","same","statistical","model","as","discretemod.Poisson",".","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 loglike(self, params):
      return -self.nloglikeobs(params).sum(0)
  
file_path: statsmodels/statsmodels/examples/ex_generic_mle_tdist.py
imports: import numpy
  from scipy import stats, special, optimize
  import statsmodels.api
  from statsmodels.base.model import GenericLikelihoodModel
  from statsmodels.tools.numdiff import approx_hess
  import statsmodels.sandbox.distributions.sppatch
label_desc: Use image node_id 1 for calling the MyT obj's underlying member method code with example usage: obj.loglike(params) and returns: unknown