class_docstr: Provides optimization functions used in estimating and conducting
  inference in an AFT model.
  
  Methods
  ------
  
  _opt_wtd_nuis_regress:
      Function optimized over nuisance parameters to compute
      the profile likelihood
  
  _EM_test:
      Uses the modified Em algorithm of Zhou 2005 to maximize the
      likelihood of a parameter vector.
class_docstr_tok: ["Provides","optimization","functions","used","in","estimating","and","conducting","inference","in","an","AFT","model",".","Methods","--","--","--","_opt_wtd_nuis_regress",":","Function","optimized","over","nuisance","parameters","to","compute","the","profile","likelihood","_EM_test",":","Uses","the","modified","Em","algorithm","of","Zhou","2005","to","maximize","the","likelihood","of","a","parameter","vector","."]
code: def __init__(self):
      pass
  
imports: import warnings
  import numpy
  from scipy import optimize
  from scipy.stats import chi2
  from statsmodels.regression.linear_model import OLS, WLS
  from statsmodels.tools import add_constant
  from statsmodels.tools.sm_exceptions import IterationLimitWarning
  from .descriptive import _OptFuncts
label_desc: Use image node_id 1 to create a new OptAFT object from inherited base classes: _OptFuncts with example: obj = OptAFT()