class_docstr: Maximum Likelihood Estimation of Poisson Model
  
  This is an example for generic MLE which has the same
  statistical model as discretemod.Poisson but adds offset
  
  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","Poisson","Model","This","is","an","example","for","generic","MLE","which","has","the","same","statistical","model","as","discretemod.Poisson","but","adds","offset","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 __init__(
      self, endog, exog=None, offset=None, missing="none", **kwds
  ):
      # let them be none in case user wants to use inheritance
      if offset is not None:
          if offset.ndim == 1:
              offset = offset[:, None]  # need column
          self.offset = offset.ravel()
      else:
          self.offset = 0.0
      super().__init__(endog, exog, missing=missing, **kwds)
  
file_path: statsmodels/statsmodels/miscmodels/count.py
imports: import numpy
  from scipy import stats
  from scipy.special import factorial
  from statsmodels.base.model import GenericLikelihoodModel
label_desc: Use image node_id 1 to create a new PoissonOffsetGMLE object from inherited base classes: GenericLikelihoodModel with example: obj = PoissonOffsetGMLE(endog, exog, offset, missing)