class_docstr: Maximum Likelihood Estimation pareto distribution
  
  first version: iid case, with constant parameters
class_docstr_tok: ["Maximum","Likelihood","Estimation","pareto","distribution","first","version",":","iid","case",",","with","constant","parameters"]
code: def nloglikeobs(self, params):
      # print params.shape
      if self.fixed_params is not None:
          # print 'using fixed'
          params = self.expandparams(params)
      b = params[0]
      loc = params[1]
      scale = params[2]
      # loc = np.dot(self.exog, beta)
      endog = self.endog
      x = (endog - loc) / scale
      logpdf = np_log(b) - (b + 1.0) * np_log(
          x
      )  # use np_log(1 + x) for Pareto II
      logpdf -= np.log(scale)
      # lb = loc + scale
      # logpdf[endog<lb] = -inf
      # import pdb; pdb.set_trace()
      logpdf[x < 1] = -10000  # -np.inf
      return -logpdf
  
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 3 for calling the MyPareto obj's underlying member method code with example usage: obj.nloglikeobs(params) and returns: unknown